The energy consumption of communications networks has been steadily increasing, becoming one of the key factors restricting the development of mobile communications. Cell-free massive multiple-input multiple-output (CF-mMIMO) systems offer wide coverage, high spectral efficiency (SE) and excellent energy efficiency (EE), and by integrating renewable energy sources, can greatly enhance their energy-saving capabilities. However, differences in energy harvesting (EH) efficiency and communications conditions across various deployment locations bring significant challenges. In this paper, we investigate the access point (AP) deployment strategy for CF-mMIMO systems with hybrid energy supply. We formulate an optimization problem aimed at maximizing grid EE and propose an AP deployment optimization method based on the deep deterministic policy gradient (DDPG) algorithm. By considering the large number of potential AP locations, aK-means-based preliminary AP selection method is introduced to significantly reduce the action space dimensionality in the DDPG framework. Simulation results indicate that the proposedK-means-based approach effectively reduces the action space dimensionality and enhances the training efficiency of the DDPG algorithm. Furthermore, the proposed method shows great effectiveness in improving the grid EE.

In the 6G era, ultra-reliable and low-latency communications (URLLC) has become a key enabler for mission-critical applications. However, existing approaches predominantly rely on closed-loop communications with hybrid automatic repeat request (HARQ) retransmissions, which struggle to meet the stringent microsecond-level end-to-end (E2E) latency requirements of 6G, particularly during the initial access phase. To bridge this gap, we propose a novel open-loop communication (OLC) framework, integrating grant-free access with frequency diversity under a contention-based transmission paradigm. The framework incorporates a fine-grained resource allocation strategy and detailed mechanisms for collision detection, uplink channel estimation, and signal detection. To evaluate the performance of OLC, we conduct a unified reliability and latency analysis, leveraging the channel hardening property of massive MIMO systems. We derive closed-form approximations for packet loss probability, incorporating factors such as access collisions, channel impairments, and finite blocklength effects. Furthermore, to minimize uplink bandwidth while satisfying reliability and latency constraints, we develop an optimal system parameter configuration method, which is seamlessly integrated with power control in the collision detection process. Simulation results validate the theoretical reliability expressions and demonstrate the superior performance of the proposed OLC framework over conventional closed-loop schemes.

Efficient resource allocation in hardware-impaired cell-free systems is critical for achieving the stringent requirements of ultra-reliable low-latency communication (URLLC) while maintaining energy efficiency (EE). Traditional optimization-based approaches face scalability issues, while existing learning-based methods struggle to adapt to varying network structures and the complexities of hardware impairments. In this work, we address these challenges by incorporating rate-splitting multiple access (RSMA) into cell-free systems and designing a graph neural network (GNN)-based framework. First, we propose a method leveraging explicit channel state information to optimize precoding for both common and private streams under rate, power, and latency constraints. Next, we further develop an end-to-end approach that bypasses channel estimation by directly using raw pilot signals for joint feature extraction and optimization. Finally, we introduce a pilot-free method that processes distorted message-passing information from real channels, reducing communications overhead while enhancing adaptability to practical conditions. Through extensive simulations, we validate the proposed methods, demonstrating significant improvements in EE, along with insights into their computational complexity and scalability in diverse system configurations.

Future wireless networks aim to deliver ultra-reliable and low-latency services while containing their rapidly growing energy footprint. In a cell-free radio access network (CF-RAN), this objective translates into a tightly coupled optimisation over access point (AP) activation, user association, precoding design and virtual-CPU provisioning, all under finite-blocklength reliability constraints. We build a detailed power model that includes radio hardware, fronthaul and load-dependent computing, then recast the resulting energy efficiency problem as a mixed-integer second-order cone programming using a tight surrogate for decoding-error probability. A sparsity-promoting convex–concave solver can reach near-optimal solutions but must be run for every channel realisation, making real-time use impractical. To overcome this limitation, we propose a graph neural network (GNN) that represents CF-RAN as a heterogeneous AP-to-user graph, predicts precoding vectors, rates and soft association probabilities in a single forward pass, and then applies a lightweight reliability-enforcement layer to remove any residual violations. Simulation results show that, whenever the constraints are feasible, the learned solver achieves comparable energy efficiency as the optimization-based baseline while operating with only a single-pass inference step per channel realization.

This work explores the maximization of energy efficiency (EE) in cell-free ultra-reliable low-latency communication (URLLC) systems, specifically addressing the challenges presented by hardware impairments (HWIs). We introduce ratesplitting multiple access (RSMA) as an effective technique to enhance EE by managing the distortions caused by HWIs during downlink transmission. The optimization problem is formulated to maximize EE by optimizing precoding vectors for both common and private streams, while adhering to rate, power, and URLLC constraints. To address the non-convex and dynamic nature of this optimization problem, we propose a graph neural network (GNN) model that facilitates scalable and data-efficient solutions. Simulation results demonstrate that the proposed RSMA-GNN method consistently outperforms baseline approaches, including space-division multiple access (SDMA) and successive convex approximation (SCA) methods, particularly in scenarios characterized by severe HWIs.

To meet the stringent requirements of latency, reliability and energy efficiency (EE), we introduce rate splitting multiple access (RSMA) into ultra-reliable and low-latency communication (URLLC) IIoT networks, where RSMA has a good flexibility in interference management. By jointly optimizing beamforming design and common rate allocation, we investigate the worst-case EE maximization problem with imperfect channel state information (CSI). Although the problem is nonconvex, we exploit the monotonicity of the problem to develop an optimal solution, where a monotonic optimization framework based on polyblock outer approximation (PA) and boundary searching is proposed to find the optimal points on the Pareto boundary. Simulation results show the convergence of the proposed optimal algorithm, and RSMA can achieve higher EE than space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) in URLLC IIoT scenarios.

In this paper, we investigate the beamforming and power control issue in cellular-connected unmanned aerial vehicle (UAV) communications under full-duplex (FD). To mitigate the self-interference (SI), we adopt a decoupled uplink (UL)-downlink (DL) association for UAVs to spatially separate the transmit and receive beams. Then, we formulate a joint beamforming and power control problem to maximize the system’s spectral efficiency (SE) while ensuring each UAV meets its UL transmission rate and DL latency requirements. To solve this problem, we design a novel distributed heterogeneous graph neural network (HGNN) architecture for beamforming and power control with a low signaling overhead. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total SE, UL transmission rate and DL latency.

Reconfigurable intelligent surface (RIS) technology has emerged as a key direction for 6 G mobile communication systems due to its revolutionary potential in channel control, coverage enhancement, and energy efficiency optimization. In this paper, a novel ray tracing (RT) channel model for RIS-based beamforming at the transmitter (Tx) side is proposed. The model considers line-of-sight (LoS) conditions for both the Tx and receiver (Rx). At the Tx side, the feed antenna directs the beam toward the RIS, ensuring that at least one reflected ray from the center of the RIS reaches the Rx. The Rx captures the reflected rays arriving from the direction of the RIS. The statistical properties of the channel model for RIS-based beamforming in indoor scenarios are analyzed including path loss, delay power spectral density (PSD), and angular PSD. The statistical properties of the proposed channel model can well matched the channel measurements.

This paper proposes a joint power and bandwidth optimization framework for ultra-reliable and low-latency communications (URLLC) in coordinated multi-point (CoMP) systems, addressing both imperfect CSI and queueing. We characterize the queueing model using effective bandwidth theory and derive the signal-to-interference-plus-noise ratio (SINR) under the channel estimation delays and errors. A block error rate (BLER) minimization problem is formulated with constraints on power, bandwidth, and URLLC QoS metrics. The problem is decomposed into bandwidth and power allocation subproblems, solved alternately via an iterative algorithm. Simulation results demonstrate that the proposed algorithm achieves over a 25% improvement in network availability compared to other benchmarks, ensuring 99.99999% reliability for all URLLC users under 1 ms latency.

Cellular-connected unmanned aerial vehicle (UAV) communications provides UAVs with ubiquitous connectivity via cellular networks. Introducing full-duplex (FD) technology into cellular-connected UAV communications to enable real-time bidirectional traffic applications, such as emergency rescue. However, severe self-interference (SI) under FD can lead to performance degradation, particularly with a coupled uplink-downlink (UL-DL) association. To address this issue, we adopt a decoupled UL-DL association (DUDA), which achieves spatial separation of transmit and receive beams to reduce SI. However, existing DUDA policies, which rely on minimum path loss (PL), are not optimal under FD. Therefore, we propose an adaptive multi-criteria decoupling scheme based on fuzzy logic (FL) to further reduce SI. In particular, we first consider the PL, the base station (BS)-UAV-BS angle, and the BS loads as the multiple criteria. We then use FL to integrate these criteria and generate scores for each BS. Finally, we can adaptively decouple the UL-DL association based on these calculated scores. Simulation results demonstrate that the proposed decoupling scheme can significantly mitigate SI and improve the average transmission rate in DL and UL.

This paper introduces a two-stage operational framework for computation offloading in the mobile edge computing (MEC)-enabled cell free massive multiple-input multiple-output (CF-mMIMO) systems. The computation offloading task is divided into data transmission and task computing sub-tasks to achieve fine-grained optimization of energy efficiency and reduce task complexity. A staged multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to solve the energy optimization problem. In the transmission stage, user equipments (UEs) act as agents to optimize the allocation of transmission power, while in the computing stage, access point (AP) clusters act as agents to optimize the allocation of computational resources. The simulation results demonstrate that compared with the baseline algorithm, the proposed scheme can achieve a significant improvement in energy consumption during the transmission stage, and near-optimal performance with lower complexity during the computing stage.

By providing macro-diversity, coordinated multipoint (CoMP) communications is promising for achieving ultra-reliable low-latency communications (URLLC). Two significant issues are considered in this paper: imperfect acquisition of channel state information (CSI) and coexistence with enhanced mobile broadband (eMBB) services. The former arises from non-negligible channel estimation delays and/or channel estimation errors, while the latter stems from the conflicting requirements of different services sharing network resources. To address these challenges, we explore a resource puncturing scheme for URLLC when the CoMP system provides URLLC and eMBB services simultaneously. Specifically, we propose a pilot-based symbol allocation framework that maximizes the number of symbols for eMBB services after puncturing, subject to constraints on the URLLC block error rate (BLER) and delay. Finally, we develop a constrained deep reinforcement learning (DRL) algorithm to obtain optimal policy for URLLC services. Simulation results demonstrate that the proposed DRL algorithm can converge quickly. Compared to two existing benchmarks, our proposed algorithm reduces the number of symbols punctured by the URLLC service while ensuring its BLER outage probability.

With the rapid evolution of 6G wireless communication technology, the granular classification of communication scenarios becomes increasingly sophisticated. This necessitates a deeper exploration of the intrinsic relationships between environmental information and channel characteristics. Consequently, the development of efficient and accurate methods for communication scenario reconstruction emerges as a critical imperative. Leveraging comprehensive three-dimensional (3D) spatial information from point cloud data, we propose a two-stage workflow for processing massive unstructured point cloud data to generate triangular mesh models of large indoor communication environments. The first stage implements an enhanced RANdom SAmple Consensus (RANSAC) algorithm with adaptive thresholding for robust wall structure extraction. Subsequently, we employ a hybrid reconstruction method combining template-based deformation for furniture elements with a zero-shot semantic segmentation network for wall opening detection. The geometric information extracted through the aforementioned process is utilized to generate mesh models, and ray-tracing (RT) is adopted to simulate channel characteristics. Finally, the efficiency and accuracy of the proposed scenario reconstruction and channel modeling method is demonstrated by comparing its simulated channel characteristics with those of channel measurements.

Beam tracking is a promising technology in mmWave-enabled cellular-connected unmanned aerial vehicle (UAV) communications. However, conventional beam tracking schemes always incur a large training overhead, and it is difficult to determine the time duration of a training cycle due to the high mobility of UAVs, which is essential for improving the effective achievable rate (EAR). To address this issue, we first adopt angular velocity estimation to obtain the beam coherence time, which serves as the time duration of the training cycles. To further reduce the training overhead in each training cycle, we then design an adaptive beam tracking algorithm based on bandit learning, where the actions are taken based on the accuracy of the angular velocity estimation. If the estimation is not accurate, more beams will be swept in the next cycle. In this way, the beam misalignment incurred by estimation inaccuracy will largely alleviate. Thus, the EAR can be effectively improved with smaller training overhead. The simulation results demonstrate the superior performance of the proposed algorithm in terms of the training overhead and the EAR.

The ultra-dense access point (AP) deployment in cell-free massive multiple-input multiple-output (CF-mMIMO) networks significantly escalates the energy burden. Equipping APs with energy harvesting (EH) capabilities can reduce grid energy comsumption by leveraging renewable resources. And strategic AP selection can further enhance energy savings without compromising quality of service (QoS) during low-traffic periods. Therefore, we formulate a problem of minimizing grid energy consumption under stochastic EH patterns and different QoS requirements, where AP selection and power allocation (PA) are taken into consideration jointly. Then, we reformulate it as a mixed-integer second-order cone programming (MISOCP) problem for optimal solutions. Given the prohibitive complexity for large-scale networks, we propose: 1) A sparsity-enhanced iterative optimization algorithm employing reweighted L1-norm relaxation to overcome the non-convexity of L0-norm optimization, and 2) A deep reinforcement learning (DRL)-based AP selection algorithm that circumvents combinatorial complexity. Simulation results show that compared to baseline algorithms, the proposed algorithms can significantly save energy consumption, which approximate the optimal solution with dramatically lower complexity.

Accurate radio map estimation is essential for the planning and optimization of wireless communication networks. However, most existing deep learning-based methods rely on an unrealistic assumption that Receive Signal Strength (RSS) measurements are uniformly distributed across the target area. To bridge this gap, we propose a trajectory-based data sampling method for practical simulation and introduce a novel representation that integrates measured RSS with wireless signal propagation models using two-channel grayscale images. With this representation, we develop the Radio Residual Denoising Diffusion Model (RRDDM) to reconstruct radio maps from sparse and trajectory-based measurements. Experimental evaluations demonstrate that RRDDM significantly outperforms existing approaches, including Deep Completion Autoencoders (Deep AE) and Radio Map Estimation via Conditional Generative Adversarial Network (RME-GAN), achieving a 20 ∼ 30% reduction in Root-Mean-Square Error under trajectory-based measurement conditions. Moreover, RRDDM also achieves superior performance in uniformly sampled scenarios, demonstrating its robustness and effectiveness in diverse measurement environments.

Full-duplex (FD) systems offers the potential for high spectral efficiency but suffers from severe self-interference (SI) and pilot overhead when using conventional channel estimation. We propose a differential modulation (DM)–based beamforming scheme for FD single-input multiple-output (SIMO) systems that eliminates pilot symbols by extracting channel information directly from received signals. Beamforming weights are adaptively updated, minimizing signaling complexity and latency. Taking into account the significant pilot overhead of coherent schemes, simulation results demonstrate that the proposed method achieves effective SI suppression and exhibits enhanced robustness in rapidly time-varying channel conditions.

In this paper, we investigate the adoption of frequency domain differential modulation to support mini-slot-assisted URLLC, which can eliminate the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Furthermore, we derive the block error rate (BLER) for frequency domain differential OFDM (FDDi-OFDM) using non-asymptotic information-theoretic bounds. Simulation results verify the accuracy of the analysis and show that, for a high Doppler environment, FDDi-OFDM can yield a much better result than the pilot-assisted coherent scheme.

Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) are two critical service types in industrial automation. In a closed-loop control system, device-to-device (D2D) communication is typically employed for direct transmission due to its low-latency requirements. However, this approach does not leverage the large-scale antenna gains of massive MIMO cellular systems. To address this limitation, we introduce a multi-connectivity network that integrates both cellular and D2D links to serve URLLC sensors while accommodating the transmission needs of general eMBB traffic. Since the D2D link serves as the primary link for URLLC transmission in a multi-connectivity setup, we first analyze the packet loss probability components for single-D2D URLLC link. Then, we formulate an optimization problem to maximize the sum channel capacity of eMBB sensors while satisfying URLLC QoS requirements. A sub-optimal power and spectrum allocation scheme is proposed to solve this coexistence problem of single-D2D URLLC and cellular eMBB transmission. For multi-connectivity, we examine the packet loss probability and present two transmission frameworks based on selection combining (SC) and maximal ratio combining (MRC). Simulation results validate the properties of the optimal solution for the relaxed problem and demonstrate the performance gains of multi-connectivity over single-D2D links.

Frequency-hopping enhanced repetition (FHER) scheme, which provides time diversity and frequency diversity, has been considered a promising approach to meet the ultra-high reliability requirement of ultra-reliable and low latency communications (URLLC) in Grant-free (GF) random access networks. However, the effects of slow fading may significantly reduce the diversity gains offered by an FHER scheme, and such effects so far have not been considered in most studies. Moreover, practical designs for FHER patterns tailored to URLLC remain underexplored. In this paper, we employ a multi-connectivity (MC) scheme to overcome the effects of slow fading and enhance reliability through space diversity. We then propose a novel FHER pattern design to meet both low latency and high reliability requirements. To support more UEs with a limited number of subchannels, we further propose an FHER pattern assignment scheme that allows pattern reuse by distant UEs while mitigating the effects of slow fading. The performance of the proposed scheme is then analyzed and approximated in closed-form expressions under both selection combining (SC) and maximum ratio combining (MRC). Simulation results show that the proposed scheme significantly increases the number of successfully served UEs compared to the K-repetition schemes. Furthermore, the simulation results closely match the analytical results under SC, while the MRC-based analysis provides a tight reliability approximation when the number of associated base stations (BSs) is small (as it is in practice).

In this article, we focus on the resource allocation and autonomous aerial vehicle (AAV) 3-D trajectory design for the AAV-ground base station (GBS) coordinated multipoint nonorthogonal multiple access (CoMP-NOMA) system to maximize the sum-rate of CoMP users while maintaining users’ high Quality of Service requirements. The main contributions of this article are summarized as follows: 1) with the assistance of closed-form power allocation result, a generalized joint user scheduling and power allocation (G-USPA) algorithm is proposed to derive the optimal user scheduling solution; 2) by revealing the monotone increasing relationship between the sum transmit power and the transmit rates of non-CoMP users, the optimal rate of each non-CoMP users turns out to be its inherent minimum required rate, consequently, the optimal transmit rates and power allocation of all users can also be derived; and 3) moreover, considering the Line of Sight (LoS) and non-LoS factors in the air-ground channel, the 3-D trajectory of AAV is designed based on successive convex approximation to provide a flexible user-centric service. The proposed G-USPA algorithm is compatible with the AAV trajectory design, which is optimized alternatively and can lead to fast convergence. Numerical results verify that the 3-D AAV-GBS CoMP-NOMA model and the G-USPA scheme have a superior performance in terms of total system sum rate and the sum rate of CoMP users over the non-CoMP AAV assisted nonorthogonal multiple access (NOMA) systems.

In this paper, we investigate a cooperative edge caching problem in the fog radio access networks (F-RANs). In order to obtain the globally optimal caching strategy that minimizes the content transmission delay and maximizes communication efficiency, we propose a multi-agent reinforcement learning based cooperative caching policy with low entropy communications. First, we propose a double deep Q network (DDQN) based caching policy by taking into account the non-deterministic polynomial hard (NP-hard) aspect of this cooperative caching optimization problem. Then, we extend the state transition model of Markov Decision Process (MDP) under the single agent system into the Stochastic Game (SG) one under the multi-agent system. By employing the DDQN in each agent, the agents can learn and make the global decision for caching. For utilizing the cooperation resources of fog access points (F-APs), the interaction of information is introduced to exchange the historical cache records of cooperative F-APs. However, the information in the interaction may require lower entropy in the fiber link. Therefore, the information entropy is largely reduced to improve the communication efficiency by quantifying the information. Finally, due to the non-computable gradient of information entropy, we apply a pseudo gradient descent method to approximate the gradient descent in the local model. Simulation results show that our policy achieves better performance in terms of reducing the transmission delay and improving the cooperation among F-APs compared to the benchmark policies. Additionally, it is demonstrated that the proposed policy improves communication efficiency without compromising the performance of cooperative caching.

The deployment of multiple reconfigurable intelligent surfaces (RIS) is a promising strategy to enhance wireless system performance. However, joint beamforming in multi-RIS assisted systems faces significant challenges due to the increased number of optimization variables, non-convex objective functions, and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and the successive convex approximation algorithm, maximizing the weighted sum rate in a double-RIS assisted downlink multi-user multiple-input single-output system. We also present a general framework for model-driven deep learning that addresses the limitations of existing methods, which often lack flexibility to different channels and suffer from a large training burden due to the high-dimensional action space of deep reinforcement learning (DRL). Initially, we configure the step size in the proposed algorithm as trainable, accelerating convergence. Then, a recurrent neural network generates the step size for iterations, allowing dynamic iteration extension in varying environmental conditions. We enhance the neural network’s self-adaptability by introducing a model-driven DRL algorithm, integrating expert knowledge into the DRL actor network’s design. Simulation results demonstrate up to 30% performance improvement over traditional algorithms, achieved by our model-driven framework. The proposed model-driven DRL shows higher capacity for dynamic extension and rapid adaptation to new environments.

One of the primary challenges in ultra-reliable and low-latency communications (URLLC) is to achieve accurate channel estimation and data detection while minimizing latency. Given the small packet size in URLLC, relying solely on pilot-assisted (PA) coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore both frequency domain differential modulation (FDDM) and time domain differential modulation (TDDM), enabling non-coherent short packet URLLC with mini-slot structures. The minimum achievable block error rate and the maximum achievable rate for all three modes (i.e., FDDM, TDDM and PA modes) are derived using non-asymptotic information-theoretic bounds. Furthermore, we show that FDDM can more than compensate for the training overhead inadequacy and performance degradation of PA mode in medium-to-high-mobility scenarios, thereby improving the performance of short packet transmission with mini-slot by dynamically activating FDDM. Simulation results validate the feasibility and effectiveness of the proposed low overhead FDDM mini-slot transmission scheme.

Ultra-Reliable and Low-Latency Communications (URLLC) is essential for sixth generation communications, with Cell-Free massive Multiple-input-Multiple-Output (CF mMIMO) being a promising architecture to support these demands. This paper addresses the challenge of optimizing energy efficiency in CF mMIMO systems for URLLC, focusing on the probabilistic delay bounds and finite blocklength communications. We propose a theoretical framework that considers tail distributions to evaluate extreme reliability and latency requirements, instead of relying on asymptotic analysis. In particular, a closed-form expression for the signal-to-interference-plus-noise ratio (SINR) distribution is derived, accommodating imperfections in channel state information caused by pilot contamination. Then, the paper also presents a comprehensive reliability analysis, incorporating both delay violation probability and average decoding error probability, utilizing stochastic network calculus for accurate statistical modeling. Finally, an innovative power control algorithm is proposed to maximize effective energy efficiency (EEE), the ratio of the effective data rate to total power consumption, while meeting stringent Quality-of-Service (QoS) constraints and power limits. Extensive simulations validate the theoretical framework and the efficacy of the proposed algorithm, demonstrating its ability to enhance EEE in various scenarios and providing insights into the interplay between EEE, delay, and reliability metrics.

This paper investigates the problem of caching placement and wireless backhaul resource allocation for enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) coexistence. Different from existing works which focus on the resource allocation for eMBB and URLLC in the access but ignore the backhaul traffic, this paper considers that the backhaul traffic can be alleviated by caching eMBB contents at the edge, such that more transmission resources can be released for scheduling URLLC traffic in wireless backhaul networks. We first propose an upper confidence bound (UCB)-based caching algorithm to reduce eMBB traffic in the backhaul, resulting in more backhaul resources that can be punctured by URLLC traffic. To minimize the URLLC delay while ensuring eMBB throughput, a greedy resource allocation algorithm is then proposed to allow URLLC traffic to puncture resources of eMBB traffic in the backhaul as many as possible. Simulation results demonstrate the superior performance of the proposed algorithms in terms of URLLC delay.

We study an unmanned aerial vehicle (UAV)-enabled uplink heavy non-orthogonal multiple access (NOMA) system in this paper, where the UL communication becomes extremely important in some hot spot areas such as live concerts or soccer stadiums, and investigate the joint optimization of bandwidth assignment (BA) and power allocation (PA) with UAV three-dimensional (3D) trajectory design to maximize the minimum average rate among all ground users, while meeting their heterogeneous rate requirements. More specifically, we propose a joint BA and PA algorithm by revealing that the inter-user interference in each NOMA group can be eliminated naturally while deriving the sum rate of users. The algorithm is proposed to get the optimal BA and PA solutions with the help of closed-form results and ellipsoid method. After that, in order to solve the UAV 3D trajectory design problem we introduce the elevation angle as a supplementary variable, and the successive convex approximation method is adopted to obtain the UAV 3D trajectory. The joint BA and PA algorithm and the UAV 3D trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

In this paper, we investigate the energy efficiency (EE) optimization in the user-centric Cell-Free massive MIMO (CF mMIMO) system for Ultra-Reliable Low-Latency Communications (URLLC), where access points (APs) use maximum ratio transmission for downlink transmission. We first formulate an optimization problem to maximize the system EE while taking the finite blocklength achievable rate in URLLC into consideration. To deal with the intractable achievable rate in the objective function and the constraint, we derive a convex lower bound of it using successive convex approximation (SCA), and then reformulate the original problem into a second-order cone programming (SOCP). Next, we propose a low-complexity iterative algorithm to solve the SOCP by applying SCA. Simulation results show that the proposed method provides near-optimal performance in terms of Branch-and-Bound (BnB), and provide insights into the influences of blocklength, system parameters and AP clustering schemes on system EE.

Due to the dense base station deployment, the high energy consumption caused by backhaul traffic is becoming increasingly serious. In this paper, a beamforming scheme is proposed to support dynamic and non-uniform uplink backhaul traffic with mmWave multiple-input-multiple-output (MIMO) arrays at the hub in a more energy-efficient manner. Assuming that the correct data link has been established during the initial access stage of backhauling, we consider beamforming for lighter backhaul traffic by forming a wider beam. The differentiation in backhaul traffic allows deactivating some phase shifters (PS) to improve energy efficiency (EE). We first formulate the optimization problem of maximizing EE, then convert latency constraints into effective rate constraints and finally introduce PS contribution to determine the deactivation sequence. The simulation results show that the strategy can improve EE by nearly 40%, demonstrating the superiority of the proposed PS deactivation strategy.

Acquiring accurate channel state information (CSI) in finite blocklength (FBL) ultra-reliable and low-latency communications (URLLC) with millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) necessitates a significant pilot overhead. To overcome this challenge, differential modulation (DM) is a promising solution for eliminating the costly pilot overheads in FBL and avoiding pilot contamination in mmWave massive MIMO systems. In this paper, we propose a combination of DM and hierarchical codebook-based beam training schemes as a feasible way to enable FBL URLLC with mmWave massive MIMO. Additionally, we derive the block error rate (BLER) of the joint DM and hierarchical codebook-based beam training scheme in the FBL regime by employing non-asymptotic information-theoretic bounds. The simulation results verify the accuracy of the analysis and show that DM does offer an advantage over the pilot-assisted coherent schemes under FBL with mmWave massive MIMO.

Multi-agent reinforcement learning (RL) has recently been adopted to solve massive ultra-reliable and low-latency communications (mURLLC) energy efficiency (EE) optimization problem in a single-cell cellular network under random access. Bursty traffic is an important characteristic of mURLLC users (UEs). This characteristic and its impact on the RL scheme are generally ignored in many RL-based studies related to the optimization of EE for uplink mURLLC. Moreover, in a smart factory with multiple cells, inter-cell interference and shadow fading further complicate EE optimization. To address these issues, we propose a novel cooperative multi-agent scheme to maximize the long-term EE in a multi-cell cellular network with mURLLC bursty traffic and a K-repetition scheme by optimizing the repetition value and transmission power. A UE clustering algorithm and an intermittent learning mode are adopted to reduce the computational complexity and mitigate the impact of bursty traffic on the RL scheme. A proper reward function is designed to address both long-term EE maximization and the number of successfully served UEs under high reliability requirement. The simulation results show that our proposed cooperative multi-agent reinforcement learning scheme greatly outperforms other existing schemes in terms of long-term accumulated EE and the number of successfully served UEs.

Industrial Internet of Things (IIoT), which contains different types of devices with heterogeneous Quality-of-Service (QoS) requirements, has encountered significant challenges on guaranteeing the needs of heterogeneous data collection utilizing limited resources. In this article, we investigate the joint frame structure and resource optimization for the hybrid long- and short-packet nonorthogonal multiple access (NOMA)-based data collection with imperfect successive interference cancellation (SIC) in IIoT, where a number of short and long packets can multiplex the same time-frequency resource simultaneously to guarantee their respective heterogeneous QoS requirements. Specifically, the short packet is first decoded to guarantee low latency, afterward the superposed long packet can be decoded to maintain high signal-to-interference-plus-noise-ratio (SINR) performance. A joint short-packet scheduling, pilot length, blocklength, and dynamic power allocation (JSLP) algorithm is proposed to minimize the maximum block error probability among short packets and mitigate the impact of SIC error propagation in NOMA transmission while maintaining a high SINR of long packet, with the assistance of the derived optimal closed-form short-packet scheduling results and pilot and block length expressions. Thanks to the closed-form expressions, the proposed JSLP algorithm demonstrates near-optimal performance and a significant complexity reduction compared to the exhaustive search, leading to fast convergence. Numerical results demonstrate that the designed hybrid NOMA-based frame structure and JSLP algorithm are robust against the SIC error propagation, and can maintain a high level of fairness by significantly mitigating the maximum block error probability gap among short packets.

Extremely large antenna array (ELAA) is regarded as one of the most crucial technologies for the next-generation communications due to its ability to significantly improve spectral efficiency. However, larger antenna aperture and higher frequency make the Rayleigh distances dramatically increased, resulting in more and more communications taking place in the near-field region. Different from traditional far-field communications, near-field communications are widely considered to be spherical wavefront-based rather than planar wavefront based, thus techniques designed for far-field scenarios might be no longer applicable. In this paper, we study the near-field communication system with an extremely large cylindrical antenna array (CLA). Under such a setup, we study the near-field beamforming, exploiting the geometrical relationship between CLA and user with the spherical-wavefront model. Specifically, we analyze the beamforming gain in the elevation angle, azimuth angle domain and distance domains, respectively. We then study the beam focusing properties in near-field CLA systems, namely the asymptotic orthogonality and the depth of focused beams. Moreover, a three-dimensional (3-D) near-field CLA codebook is proposed to make beam focusing more effective. Simulation results demonstrate that the proposed near-field codebook can effectively focus the beam to a certain location and thus improve the system achievable rate.

In this paper, the cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) nature of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a communication-efficient federated deep reinforcement learning (CE-FDRL) method to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communication resources caused by model transmission, we propose to prune and quantize the shared DRL models to reduce the number of transferred model parameters. The communication interval is increased and the communication round is reduced by periodic model aggregation. The global convergence and computational complexity of our proposed method are also analyzed. Simulation results verify that our proposed method can offer better performance in reducing user request delay and improving cache hit rate and the transmitted parameters of our proposed method can drop to 60% compared to the existing benchmark schemes. Our proposed method is also shown to have faster training speed and higher communication efficiency.

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.

Asymmetric mmWave massive MIMO systems have attracted much interest due to the massive available spectrum and lower power consumption. In these systems, once a communication link has been established in the beam training stage, part of the array can be deactivated to support asymmetric uplink-downlink (UL-DL) traffic in the data transmission stage. For this array with deactivated elements, however, the original ideal codeword requires further compensation to overcome the pattern distortion caused by mutual coupling between the activated and deactivated elements. We therefore propose a virtual impedance method, which computes a coupling compensation matrix based on a virtual impedance. We then use the compensation matrix to refine the original codeword and correct the distortion of beam pattern resulting from mutual coupling due to the partially deactivated elements. Simulation results show that the proposed virtual impedance method can not only make up for the angular distortion but also increase the gain in the desired direction.

Asymmetric mmWave massive multiple-input multiple-output (MIMO) systems have attracted much interest due to the massive available spectrum and lower power consumption. In these systems, once a communication link has been established in the beam training stage, part of the array can be deactivated to support asymmetric uplink-downlink (UL-DL) traffic in the data transmission stage. For this array with deactivated elements, however, the phase of the original ideal codeword requires further perturbation to overcome the pattern distortion caused by mutual coupling between the activated and deactivated elements. We therefore propose a phase perturbation method, which perturbs the phase of ideal codeword based on the effective channel including the coupling effect. In this method, we propose a fast search (FS)-based alternative optimization algorithm that alternatively adjusts each activated antenna phase to apply to the effective channel. Simulation results show that the proposed phase perturbation method can make up for the angular distortion.

We consider a three-dimensional (3D) unmanned aerial vehicle (UAV)-terrestrial base station (TBS) coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) scheme where UAV coordinates with TBS to allow joint transmission for the terrestrial users. With the assistance of closed-form power allocation derivations, a joint user scheduling and power allocation (J-USPA) algorithm is proposed to obtain the optimal user scheduling solution, with the consideration of imperfect channel estimation. Moreover, considering the line of sight (LoS) and non-LoS factors in the air-ground channel, the 3D trajectory of UAV is designed to provide a flexible user-centric service. Numerical results verify that the 3D UAV-TBS CoMP-NOMA model and the J-USPA scheme have a superior performance in terms of sum rate of users over the TBS CoMP-NOMA and the UAV assisted NOMA systems without CoMP transmission.

In this paper, we consider a unmanned aerial vehicle (UAV)-enabled uplink (UL) heavy non-orthogonal multiple access (NOMA) system where the UL communication becomes increasingly important in some hot spot regions such as live concerts or football stadiums, and study the maximization of the minimum average rate among all users by jointly optimizing bandwidth assignment (BA) and power allocation (PA) alongside UAV trajectory design, while meeting their specified heterogeneous rate requirements. Specifically, by revealing that the inter-user interference can be naturally eliminated while deriving the sum rate of users in each NOMA group, an one-step BA and PA algorithm is proposed, with the assistance of closed-form results. Afterwards, the elevation angle is introduced as an auxiliary variable to help solve the UAV trajectory design problem. The joint BA and PA algorithm and the UAV trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

With the highly increasing demands of vehicular applications, the cloud intelligence is pushed towards the edge by placing services next to vehicle users. Due to the limited resources of edge nodes and the high mobility of vehicle users, it is challenging to place vehicular services effectively at the edge to serve vehicle users with high quality of experience (QoE). In this paper, we investigate a vehicular service placement problem with unknown demands. Different from previous works, we consider that vehicle users have various service demands, which are related to their contexts. To enable on-demand service placement, we propose a context-aware vehicular service placement algorithm based on the estimated service demands. For better demand estimation, a fine-grained partition method is developed to divide the context space. The simulation results show that the proposed algorithm has superior performance in terms of cumulative system rental utility.

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfil the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resource of fog access points (F-APs) and also reduce the communication overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The proposed quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communication overhead effectively. Simulation results show that the performance of our proposed policy outperforms the considered baseline policies.

In this paper, a beamforming scheme that aims to support user-wise asymmetric uplink-downlink (UL-DL) traffic in a more energy-efficient manner is proposed for time-division duplex (TDD) millimeter-wave massive multiple-input-multiple-output (MIMO) systems. Assuming that proper data links have been established during the initial access stage for DL or UL traffic, we consider the beamforming problem for UL or DL whose payload traffic is much lighter than the other link. Such asymmetric traffic allows part of the massive MIMO array to be deactivated in order to achieve a higher energy efficiency (EE) while still meeting the spectrum efficiency (SE) requirements for UL or DL. To deal with such a problem, we propose the corresponding phase shifter (PS) deactivation strategies based on different SE constraints for individual mobile stations or users independently, which select the PSs to be deactivated accordingly with low computational complexity. We then propose a novel codebook design method, which relies on the iteratively updated average main beam gain, in order to pursue a flatter beam under asymmetric DL-UL traffic that interacts with the corresponding PS deactivation strategy. The proposed codebook not only offers flexible beam width and flat main beam gain, but also can generate some good candidate codewords to account for the need of beam refinements, after some of the PSs have been deactivated. Simulation results demonstrated the superiority of the proposed energy efficient PS deactivation approach and the corresponding codebook design.

In this paper, we investigate a hybrid caching strategy maximizing the success transmission probability (STP) in a millimeter wave (mmWave) cache-enabled network. First, we derive theoretical expressions of the STP by utilizing stochastic geometry, then we consider the maximization of the STP by optimizing the design parameters. Considering the optimality structure of the NP-hard problem, the original problem is transformed into a multi-choice knapsack problem (MCKP). Finally, we investigate the impact of key network parameters on the STP. Numerical results demonstrate the superiority of the proposed caching strategy over the conventional caching strategies in the mmWave cache-enabled networks.

In this paper, a beamforming scheme that aims to support user-wise asymmetric uplink-downlink (UL-DL) traffic in a more energy-efficient manner is proposed for time-division duplex (TDD) millimeter-wave massive multiple-input-multiple-output (MIMO) systems. Assuming that proper data links have been established during the initial access stage for DL or UL traffic, we consider the beamforming problem for UL or DL whose payload traffic is much lighter than the other link. Such asymmetric traffic allows part of the massive MIMO array to be deactivated in order to achieve a higher energy efficiency while still meeting the spectral efficiency (SE) requirements for UL or DL. To deal with such a problem, we propose the corresponding phase shifter (PS) deactivation strategies based on different SE constraints for each mobile stations or user independently, which select the PSs to be deactivated accordingly with low computational complexity. Simulation results demonstrated the superiority of the proposed energy efficient PS deactivation approach and the corresponding codebook design.

Reconfigurable intelligent surface (RIS) and index modulation (IM) are two emerging technologies, which show great potentials to achieve green and clean wireless communications, attracting extensive attention in recent years. This paper designs and implements an RIS-based IM transmission scheme that effectively integrates the two techniques. By utilizing the characteristics of RIS to realize flexible control of electromagnetic waves in a reconfigurable manner, IM wireless transmission can be directly realized without conventional radio frequency chains. The proposed approach is validated through the prototype system which is set up based on a fabricated phase-programmable RIS operating in the sub-6GHz frequency band. The experimental results convincingly verify the feasibility of the proposed scheme and suggest that RISs offer a cost-effective hardware architecture to realize IM with massive transmitting antennas.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content transmission delay, we formulate the cooperative caching optimization problem to find the globally optimal caching strategy. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a Multi Agent Reinforcement Learning (MARL)-based cooperative caching scheme is proposed. Our proposed scheme applies a double deep Q-network (DDQN) in every fog access point (F-AP), and introduces the communication process in a multi-agent system. Every F-AP records the historical caching strategies of its associated F-APs as the observations of communication procedure. By exchanging the observations, F-APs can leverage the cooperation and make the globally optimal caching strategy. Simulation results show that the proposed MARL-based cooperative caching scheme has remarkable performance compared with the benchmark schemes in minimizing the content transmission delay.

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated to jointly optimize the transmission delay and energy consumption. Exploiting the potential social relationships among fog access points (F-APs), we firstly propose a clustering scheme based on hedonic coalition game (HCG) to improve the potential cooperation gain. Then, considering that the optimization problem is non-deterministic polynomial hard (NP-hard), we further propose an improved firefly algorithm (FA) based cooperative caching scheme, which utilizes a mutation strategy based on local content popularity to avoid pre-mature convergence. Simulation results show that our proposed scheme can effectively reduce the content transmission delay and energy consumption in comparison with the baselines.

In this paper, we investigate maximum distance separable (MDS) codes based group coded caching in fog radio access networks (F-RANs). The goal is to minimize the average fronthaul rate under nonuniform file popularity. Firstly, an MDS codes and file grouping based coded placement scheme is proposed to provide coded packets and allocate more cache to the most popular files simultaneously. Next, a fog access point (F-AP) grouping based coded delivery scheme is proposed to meet the requests for files from different groups. Furthermore, a closed-form expression of the average fronthaul rate is derived. Finally, the parameters related to the proposed coded caching scheme are optimized to fully utilize the gains brought by MDS codes and file grouping. Simulation results show that our proposed scheme obtains significant performance improvement over several existing caching schemes in terms of fronthaul rate reduction.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. Initial features of local users and contents are generated by incorporating neighbor information with self information. Then, dual-channel neural network (DCNN) model is introduced to learn the hidden representations by producing deep latent features from initial features. For mobile users, the content popularity is predicted via user preference learning. In order to distinguish regional variations of content popularity, clustered federated learning (CFL) is employed, which enables fog access points (F-APs) with similar regional types to benefit from one another and provides a more specialized DCNN model for each F-AP. Simulation results show that our proposed policy achieves significant performance improvement over the traditional policies.

Reconfigurable intelligent surface (RIS) is a potential solution to cost-effectively and energy-efficiently improve the performance of the sixth generation (6G) wireless communication system. In this paper, a method to realize the function of RIS in three dimensional (3D) ray tracing simulator is proposed and applied to two indoor scenarios where there is no line-of-sight (LoS) path between transmitter (Tx) and receiver (Rx). To verify the effectiveness of the proposed method, channel characteristics including received power, channel capacity, and angular power spectral density (PSD) are studied. To analyze the influence of different RIS deployment locations on coverage enhancement, simulations with different setups are carried out at 5.4 GHz. Besides, applicability of RIS in millimeter wave (mm Wave) is also demonstrated in ray tracing simulator. Numerical results show a significant coverage improvement by adding RIS into current wireless communication system and reveal useful insights for optimal RIS deployment in indoor scenarios.

In recent years, optimization of the successful transmission probability (STP) in wireless cache-enabled networks has been studied extensively. However, few works have examined the real-time performance of the cache-enabled networks. In this paper, we investigate the performance of the cache-enabled networks with real-time guarantees by adopting age of information (AoI) as the metric to characterize the timeliness of the delivered information. We establish a spatial-temporal model by utilizing stochastic geometry and queueing theory which captures both the temporal traffic dynamics and the interferers’ geographic distribution. Under the random caching framework, we achieve the closed-form expression of AoI by adopting the maximum average received power criterion for the user association. Finally, we formulate a convex optimization problem for the minimization of the Peak AoI(PAoI) and obtain the optimal caching probabilities by utilizing the Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the random caching strategy is a better choice than both the most popular caching (MPC) and uniform caching (UC) strategies when it comes to improving the real-time performance for the cached files as well as maintaining the file diversity.

In this paper, we consider the dynamic content caching issue in the cache-enabled Internet of Things (IoT) systems. For real-time applications in cache-enabled IoT systems, it is imperative to design dynamic content caching schemes to reduce the energy consumption of sensors and improve the freshness of information at users. We first design a dynamic content caching procedure for a cache-enabled IoT system with limited cache capacity and express the evolution of the Age of Information (AoI) at both the edge caching node and each user. Then, we formulate the dynamic content caching problem as a Markov Decision Process to minimize the expectation of a long-term accumulative cost, which jointly considers the average AoI of users and the energy consumption of sensors. To solve this problem, we propose an actor-critic based caching algorithm without prior knowledge of users’ content demands. The numerical results show that the proposed algorithm can achieve lower average AoI and energy consumption than other baselines.

Cellular networks are promising for unmanned aerial vehicle (UAV) communications. In this paper, we contribute to this emerging area by focusing on delay measurements of drones connecting to commercial LTE and 5G networks in real-world scenarios, which are rarely reported in prior works. Measurements are carried out in two typical scenarios, i.e., suburban environment and urban environment. Different delay behaviors are observed in these two scenarios. By analyzing the impact of different parameters on delay performance, we find that the average delay is highly related to SINR, while the delay variance (delay fluctuation) shows a strong relation to handover frequency in both scenarios. The gained insights could provide some guidelines for integrating aerial users to cellular networks.

In this work, we optimize the repetition scheme for ultra-reliable low latency uplink communications. We consider grant-free access in the frequency domain, where a user transmits multiple repetitions of a packet over shared subchannels in a single time slot. To save transmission energy and to decrease collision probability, we optimize the number of repetitions subject to the diverse reliability requirements. In our optimization framework, different users may have different reliability requirements and different active probabilities depending on the user services. The optimization problem turns out to be an integer-programming problem with high complexity. We design a low-complexity algorithm to solve the optimization problem. Simulation results show that the proposed scheme can save uplink transmission energy by 10 ~ 30% and can reduce the infeasible probability remarkably compared with the K-repetition scheme. The infeasible probability is defined as the probability that the reliability requirement cannot be satisfied with the given resources.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for the next generation networks. By utilizing tools from stochastic geometry, we develop a meta distributed-based analytical framework to study the effect of the large-scale deployment of the RIS on the performance of a millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) are modeled as Poisson point processes (PPPs). In addition, the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. By considering the randomness of the locations and orientations of the RISs and the particular characteristics of mmWave communications, we provide a statistical characterization of the path loss for the BSs and RISs and derive the analytical expressions for the k-th moment of the conditional success probability, the area spectral efficiency and the energy efficiency. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs.

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and the meta distribution can be obtained in a recursive manner. The numerical results reveal the impact of the key network parameters, such as the SINR threshold and the blockage parameter, on the network performance.

Grant-free (GF) access is expected to support low-latency services in fifth-generation (5G) systems, while non-orthogonal multiple access (NOMA) has been proposed to enable massive connectivity in cellular networks. However, the performance analysis for the GF access mode based on NOMA is not trivial, especially for large-scale multi-cell networks due to the inherent random near-far phenomenon. In this paper, we exploit tools from stochastic geometry to develop a tractable framework for analysing uplink performance in large-scale multi-cell networks under GF NOMA and short packet transmission. To make the framework tractable, we further transform the intra- and inter-cell interference to an equivalent interference model. The URLLC performance of GF NOMA networks is derived under the assumption of perfect successive interference cancellation (SIC) and short packet transmission. Numerical results obtained from theoretical calculations and Monte Carlo simulations verify the correctness of our analysis.

In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter$V$. A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as$[\mathcal {O}(1/V),\mathcal {O}(V)]$. Since the value of$V$can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for millimeter wave (mmWave) networks. In this paper, we utilize tools from stochastic geometry to study the performance of a RIS-assisted mmWave cellular network. Specifically, the locations of the base stations (BSs) and the midpoints of the blockage are modeled as two independent Poisson point processes (PPPs), where the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. The particular characteristics of mmWave communications, i.e., directional beamforming and different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) propagation, are incorporated into our analysis. We derive analytical expressions for the success probability and the area spectral efficiency. The success probability under the special case where the blockage parameter is sufficiently small is also derived. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs. In addition, the tradeoff between the BS and RIS densities is investigated and the results show that the RISs can indeed enable the traditional networks to improve the success probability, especially for the cell-edge region, with limited power consumption.

We investigate a multi-device ultra-reliable low-latency communication system with heterogeneous traffic and finite block length over temporally-correlated fading channels. In light of the challenging demand for accurate channel estimation with limited pilot in a short frame, two frame structures, which respectively adopt independent pilots and shared pilot, are investigated. Block lengths and pilot lengths are jointly optimized for the two frame structures, through instantaneous channel state information (CSI) based dynamic optimization and statistical CSI based static optimization, to strike the tradeoffs among performance, complexity and signaling overhead. The proposed joint optimization algorithms significantly outperform the existing approaches that solely optimize block lengths or pilot lengths. The dynamic optimization algorithms achieve near-optimal performance at dramatic complexity reduction over exhaustive search, and maintain robustness against traffic heterogeneity. Also, the static optimization algorithms are conducted offline, while still outperforming the previous instantaneous CSI based dynamic optimization approaches. It is demonstrated that the independent-pilot frame structure with dynamic optimization is preferable in the scenario with high traffic heterogeneity or high mobility, and that the shared-pilot frame structure with static optimization presents a comparable performance to the former in the case of low mobility, incurring negligible complexity and signaling overhead.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based onfederated learning(FL). Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters by using the stochastic variance reduced gradient (SVRG) algorithm. Finally, FL based model integration is proposed to learn the global popularity prediction model based on local models using the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only can predict content popularity accurately, but also can significantly reduce computational complexity. Moreover, we theoretically analyze the convergence bound of our proposed FL based model integration algorithm. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to existing policies.

In this paper, we investigate maximum-distance separable (MDS) codes and weighted graph based coded caching in fog radio access networks (F-RANs). In the placement phase, the redundant MDS based coded placement scheme is used to provide redundant coded packets and homogeneous cached contents. The redundant coded packets can be used to construct multicast opportunities for similar requests. In the delivery phase, the weighted graph based coded delivery scheme is conducted based on homogeneous cached contents, which can induce considerable multicast opportunities. By integrating the above two schemes, a joint MDS codes and weighted graph based coded caching policy is proposed to minimize the fronthaul load. Finally, we theoretically analyze the performance of the proposed policy by deriving the lower and upper bounds of the fronthaul load. Simulation results show that our proposed policy can provide 44% savings in the fronthaul load compared to the MDS-based uncoded delivery policy.

A fine-grained analysis of the cache-enabled networks is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference ratio for the cache-enabled networks where the locations of the base stations are modeled as a Poisson point process. With the application of the random caching and the random discontinuous transmission schemes, we derive the moments of the conditional successful transmission probability, the exact meta distribution and its beta approximation by utilizing stochastic geometry. The closed-form expressions of the mean and variance of the local delay (i.e., the jitter) are also derived. We then consider the maximization of the mean successful transmission probability and the minimization of the average system transmission delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of successful transmission probability, successful transmission probability variance, meta distribution, mean local delay and jitter.

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based Poisson regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our developed model. Then, we utilize Bayesian learning to learn the model parameters, which are robust to over-fitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a Stochastic Variance Reduced Gradient Hamiltonian Monte Carlo (SVRG-HMC) to approximate the posterior distribution. Two types of predictive content popularity are formulated for the requests of existing contents and newly-added contents. Simulation results show that the performance of our proposed policy outperforms the policy based on other Monte Carlo based method.

In this paper, we investigate an unmanned aerial vehicle (UAV) assisted communication system for uplink (UL) heavy scenarios such as a football match, where intensive video uploading from the audience is demanded. Non-orthogonal mul-tiple access (NOMA) is exploited to enhance system throughput, while it suffers a dynamic user rate gap within each NOMA group due to UAV movement. In light of the user fairness issue and the UL heavy demands, we propose joint UL-DL optimization of user scheduling, power allocation (PA) and UAV trajectory (JOSPT). In particular, closed-form optimal solutions are derived for dynamic PA in both UL and DL, which plays a dominant role in system performance. The proposed PA scheme is more effective in maintaining user fairness than the previous work and also computationally efficient and suitable for energy-limited UAV system. The average spectrum efficiency of the proposed NOMA UAV system in UL and DL is also much higher than that of the orthogonal multiple access (OMA) based UAV system, with the heterogeneous rate demands accommodated.

In this paper, we focus on the local delay for the cache-enabled networks where the locations of the base stations (BSs) are modeled as a Poisson point process (PPP). With the application of the random caching and the random discontinuous transmission (DTX) schemes, we derive the closed-form expression of the mean local delay. We then consider the minimization of the mean local delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of mean local delay.

In this paper, we propose a deep neural network (DNN) based beamforming scheme for frequency-division-duplex (FDD) millimeter-Wave (mmWave) massive multipleinput multiple-output (MIMO) systems. Different from the time-division-duplex (TDD) systems, for FDD systems the channel reciprocity between the down-link (DL) and up-link (UL) channels does not hold in general, requiring an extra channel state information (CSI) feedback stage. Based on the previous theoretical analysis and measurements, however, partial reciprocities, including the spatial directional angles and number of propagation paths, do exist for FDD mmWave systems. With this partial reciprocity, we propose a multi-path beamforming scheme with a predefined codebook. Different from most previous works that only focus on one dominant path of each mobile station (MS), this work considers a multi-path scenario where the proposed scheme identifies all the propagation paths of all MSs, and selects the optimal combination of codewords with the help of a DNN that not only overcomes angle ambiguity but also significantly reduces computational complexity.

For a multi-cell OFDM downlink network, a basic problem is to perform resource allocation to maximize the spectral efficiency (SE). Doppler shift, however, leads to a loss of subcarrier orthogonality, resulting in inter-carrier interference (ICI), especially in a high speed environment. In this paper, we solve the resource allocation problem by considering ICI caused by Doppler spread and imperfect channel state information (CSI) caused by estimation errors, quantization errors and feedback delay. However, the resultant resource allocation algorithm is so complicated that it may not be applicable to the wireless communications environment under high mobility since it may change rapidly and therefore needs real-time computation. As such we propose a deep neural network (DNN) approach to approximate the resource allocation algorithm, which greatly reduces the computation time while achieving very good prediction accuracy. Simulation results verify the influence of Doppler shift on the SE performance and the effectiveness of DNNs in terms of computing time.

The vision for smart city imperiously appeals to the implementation of Internet-of-Things (IoT), some features of which, such as massive access and bursty short packet transmissions, require new methods to enable the cellular system to seamlessly support its integration. Rigorous theoretical analysis is indispensable to obtain constructive insight for the networking design of massive access. In this paper, we propose and define the notion of massive and sporadic access (MSA) to quantitatively describe the massive access of IoT devices. We evaluate the temporal correlation of interference and successful transmission events, and verify that such correlation is negligible in the scenario of MSA. In view of this, in order to resolve the difficulty in any precise spatio-temporal analysis where complex interactions persist among the queues, we propose an approximation that all nodes are moving so fast that their locations are independent at different time slots. Furthermore, we compare the original static network and the equivalent network with high mobility to demonstrate the effectiveness of the proposed approximation approach. The proposed approach is promising for providing a convenient and general solution to evaluate and design the IoT network with massive and sporadic access.

Emergence of various types of wireless applications has brought about fast growing and diversified traffic in cellular networks. To gain a comprehensive understanding of the influences caused by the differentiated and dynamic traffic is vital for the design of the next-generation wireless networks. In this paper, we develop a mathematical framework for meta-distribution analysis by utilizing queueing theory and stochastic geometry to capture the spatial (geographical location) and temporal randomness (queue status) of traffic. We derive the${k}$-th moment of the conditional successful transmission probability (STP) and the closed-form expressions of the meta distribution for the cell-center users (CCUs) and the cell-edge users (CEUs), respectively. The results are further extended to obtain the analytical expression of the meta distribution by taking the temporal random arrival of traffic into consideration. Moreover, the mean local delays for the CCUs and CEUs are derived. Finally, the impact of key network parameters on the meta distribution and the corresponding mean local delay is investigated.

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

In this paper, we investigate the short frame structure optimization in terms of shared-pilot length and finite block length (FBL) for an industrial Internet-of-Things (IIoT) system with heterogeneous traffic requirements on latency, reliability and information size. Both dynamic and static optimization approaches are investigated to allow trade-offs between performance, complexity and signaling overhead. Effective throughput maximization problems are formulated based on statistical and instantaneous channel state information (CSI), respectively, and their monotonicities are proved. A statistical CSI based static joint block length and shared-pilot length (S-JBSPO) algorithm is proposed, which is conducted offline. With no spectral overhead and very low complexity, S-JBSPO outperforms the existing instantaneous CSI based approaches from medium to high SNR. An instantaneous CSI based dynamic JBSPO (D-JBSPO) algorithm is proposed, which maintains a near-optimal and robust throughput performance against a wide range of traffic requirements, and significantly outperforms the previous approaches, thanks to a much higher degree of freedom. D-JBSPO also demonstrates a significant performance gain over S-JBSPO, regardless of the Doppler frequency and the number of devices.

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content request delay, we formulate the hierarchical cooperative caching optimization problem to find the optimal caching policy. Considering the non-deterministic polynomial hard (NP-hard) property of this problem, we propose a brain storm optimization (BSO) approach which utilizes the penaltybased fitness function in individuals evaluation to meet the storage capacity constraint and the genetic algorithm (GA) in new individuals generation to meet the integer constraint, respectively. To further reduce the computational complexity, we propose to implement the convergent operation in the objective space via individuals classification. Simulation results show that our proposed BSO-based hierarchical cooperative caching policy achieves remarkable performance in minimizing the content request delay.

To alleviate the ever-increasing data demands, edge caching plays a crucial role in improving the performance of system, especially in data-intensive applications. Previous works mainly focus the caching policy over reliable channels. For unreliable channel scenarios, the system performance is jointly affected by the user preference and the channel reliability, whereas both the user preference and the reliability are unknown commonly. A high retrieval cost may be incurred on unreliable channels even when the requested content is in the nearby cache. To solve the issues mentioned above, we jointly optimize the service scheduling policy and the content caching policy in this paper. We propose a maximal reward priority (MRP) policy to serve user requests, and a collaborative multi-agent actor critic (CMA-AC) policy to update the local cache. Simulation results show that the proposed MRP policy outperforms the shortest distance priority (SDP) policy [4]. And the proposed CMA-AC policy obtains a better performance compared with a distributed multi-agent deep Q-network (DMA-DQN) policy, especially when the number of contents and the capacity of local cache are large. Furthermore, the proposed CMA-AC policy is robust.

In this paper, we propose a blind direct current bias (DCB) based timing synchronization and a blind null subcarrier (NS) based timing synchronization methods for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communications (VLC) systems. This is the first work to investigate blind timing synchronization for DCO-OFDM VLC systems, achieving high bandwidth efficiency, unlike the previous works which require a number of pilots. The two blind approaches are robust against the limited bandwidth of light emitting diode (LED), as the timing synchronization is conducted in frequency domain to mitigate the effect of inter-symbol-interference (ISI) caused by LED limited bandwidth, rather than being performed in time domain as the previous works that are vulnerable to the ISI. The DC bias is utilized by the proposed DCB based approach to perform blind timing synchronization, and the null subcarrier is used by the proposed NS based approach. Simulation results show that the proposed blind DCB and NS timing synchronization approaches significantly outperform the state-of-the-art methods in terms of the probability of false detection and bit error rate (BER), and yield BER performance close to ideal case with perfect synchronization, zero forcing (ZF) equalization and perfect channel state information (CSI).

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning. Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users' context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.

In this paper, a hybrid multi-user massive MIMO system operating on the mmWave frequency is considered. To reduce the heavy beam training overhead for such a massive MIMO system, we propose a 3D beam training algorithm, which estimates the azimuth and elevation of the angle of arrival (AoA) and departure (AoD) between the base station (BS) and the mobile stations (MSs). The proposed algorithm first executes a hierarchical search to acquire the range of azimuth and elevation, then applies an exhaustive search within this range for a more precise matching. Through this two-stage approach, the beam training overhead can be substantially reduced.

Mobile edge computing (MEC) has shown its potential in serving computation intensive tasks via offloading. However, the heterogeneity of MEC systems and the dynamic nature of wireless environment pose a great challenge to the design of offloading policies. In this paper, we investigate this computation offloading problem, where the heterogeneities of computational resource, channel state, task type and input data size are considered. We first propose a greedy algorithm, in which each arrival task is greedily offloaded to the edge server with minimal utility, based on a global information of network states. While this greedy algorithm performs well in terms of system utility, the overhead incurred to collect the global information is large, especially in dense MEC scenarios and time-varying channel scenarios. Inspired by this observation, we then propose a model-free offloading algorithm based on reinforcement learning, which does not rely on such kind of information and can make offloading decisions based on learning experience. By so doing, the communication overhead can be largely reduced. Extensive simulations show that the two proposed algorithms have similar performance in terms of system utility and can decrease the system utility by up to 50% compared with two widely used algorithms. The robustness of the two proposed algorithms is further verified.

Considering a multi-cell OFDM downlink network, a basic problem is to perform resource allocation to maximize the spectral efficiency (SE). In this paper, we divide it into a user scheduling subproblem and a power allocation subprolem, and then adopt a resource allocation algorithm based on imperfect channel state information (CSI). Universal frequency reuse is considered, and the cochannel interference is dealt with via the cooperation of multiple base stations (BSs) sharing CSI but not user data. Since the wireless communication environment may change rapidly and need real-time computation, we then propose a deep neural network (DNN) approach to approximate the resource allocation algorithm, which greatly reduces the computation time and is capable of ”on-the-fly” adaptation to a time-varying environment. Simulation results verify the effectiveness of the DNN implementation, especially when the number of cells and subcarriers is large.

Cooperative caching between multiple small base stations (SBSs) plays a critical role for easing the traffic congestion of the backhaul link. Previous works assume that cooperative caching policies can achieve good performance when each user in the region is mainly served by its nearest SBS, which may not be the case in small cell networks with heterogeneous channel qualities. In this paper, we study cooperative caching problem in a small cell network with heterogeneous channel qualities when content popularity profile is unknown. These two features impose new challenges in optimizing content placement in multiple SBSs. To address this problem, we first propose a bayes-based learning algorithm that learn the popularity profile by sampling from a Beta distribution at each time period. Based on the estimated popularity profile, we then optimize the content placement at each time period by caching contents with higher popularity/size ratio in SBSs with better channel qualities. Numerical results show that the proposed algorithms outperforms three baselines in terms of average transmission delay and cache hit rate.

In this paper, we jointly explore random caching and cooperative transmission in heterogenous networks (HetNets) with random discontinuous transmission (DTX). We consider a realistic scenario where joint transmission is not always available and assume two cases depending on whether joint transmission is available. With the help of stochastic geometry, a tractable expression for the average successful transmission probability (STP) is obtained. We then formulate the STP optimization problem to find the optimal caching policy. In addition, we analyze the STP under random DTX. Compared with several existing caching policies in the previous works, we show that the optimal caching policy indeed achieves a significant performance gain.

In this paper, we consider the interference detection and resource allocation issue in Long-Term Evolution Unlicensed (LTE-U) system with carrier aggregation (CA). First, to avoid the co-channel interference between the WiFi and LTE-U users, we adopt the logistic regression method to train a classifier model for the base stations (BSs) to find the users that are susceptible to the interference from the WiFi. Then, we formulate the optimization problem with the goal to maximize the downlink (DL) throughput while guaranteeing the quality-of-service (QoS) for each user. To make the original problem more tractable, we first split it into two sequential subproblems and then propose a dual decomposition method to solve them efficiently. The numerical results show that the proposed schemes can significantly improve the overall throughput and outperform the existing schemes.

A novel beam domain channel model (BDCM) for massive multiple-input multiple-output (MIMO) communication systems has been proposed in this paper. The near-field effect and spherical wavefront are firstly assumed in the proposed model, which is different from the conventional BDCM for MIMO based on the far-field effect and plane wavefront assumption. The proposed novel BDCM is the transformation of an existing geometry-based stochastic model (GBSM) from the antenna domain into beam domain. The space-time non-stationarity is also modeled in the novel BDCM. Moreover, the comparison of computational complexity for both models is studied. Based on the numerical analysis, comparison of cluster-level statistical properties between the proposed BDCM and existing GBSM has shown that there exists little difference in the space, time, and frequency correlation properties for two models. Also, based on the simulation, coherence bandwidths of the two models in different scenarios are almost the same. The computational complexity of the novel BDCM is much lower than the existing GBSM. It can be observed that the proposed novel BDCM has similar statistical properties to the existing GBSM at the cluster-level. The proposed BDCM has less complexity and is therefore more convenient for information theory and signal processing research than the conventional GBSMs.

Nonorthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and high data-rate requirements of the next-generation networks. In this article, we investigate the joint subchannel assignment and power allocation problem in an uplink multiuser NOMA system to maximize the energy efficiency (EE). Different from conventional model-based resource allocation methods, we propose three deep-reinforcement-learning (DRL)-based frameworks to solve this nonconvex optimization problem, referred to as the discrete DRL-based resource allocation (DDRA) framework, continuous DRL-based resource allocation (CDRA) framework, and joint DRL and optimization resource allocation (DORA) framework. Specifically, for the DDRA framework, a multi-DQN-based network is designed to dynamically allocate resources discretely, which can reduce the output dimension and improve the learning efficiency. To overcome the loss of power discretization in DDRA, a joint DQN and deep deterministic policy-gradient (DDPG)-based network (CDRA framework) is designed to generate the resource allocation policy. The DORA framework is then proposed as a performance boundary. Finally, an event-triggered learning method is combined with all three frameworks to further reduce the computational consumption. The numerical results show that the proposed frameworks can improve the EE performance of the uplink NOMA system and reduce the computation time.

We propose a miniaturized wideband metasurface antenna for 60-GHz antenna-in-package applications. With the glass integrated passive device manufacturing technology, we introduce a coplanar-waveguide-fed (CPW-fed) ring resonator to characterize the material properties of the glass substrate. The proposed antenna is designed on a high dielectric constant glass substrate to achieve antenna miniaturization. Because of the existence of gaps between patch units compared with the conventional rectangular patch in the TM 10 mode, the radiation aperture of this proposed antenna is reduced. Located right above the center feeding CPW-fed bow-tie slot, the metasurface patch is realized, supporting the TM 10 mode and antiphase TM 20 mode simultaneously to improve the bandwidth performance. Using a probe-based antenna measurement setup, the antenna prototype is measured, demonstrating a 10-dB impedance bandwidth from 53.3 to 67 GHz. At 60 GHz, the antenna gain measured is about 5 dBi in the boresight direction with a compact radiation aperture of 0.31 λ ×0.31 λ 0 and a thickness of 0.06 λ 0 .

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.

In this paper, we propose a novel secure random caching scheme for large-scale multi-antenna heterogeneous wireless networks, where the base stations (BSs) deliver randomly cached confidential contents to the legitimate users in the presence of passive eavesdroppers as well as active jammers. In order to safeguard the content delivery, we consider that the BSs transmits the artificial noise together with the useful signals. By using tools from stochastic geometry, we first analyze the average reliable transmission probability (RTP) and the average confidential transmission probability (CTP), which take both the impact of the eavesdroppers and the impact of the jammers into consideration. We further provide tight upper and lower bounds on the average RTP. These analytical results enable us to obtain rich insights into the behaviors of the average RTP and the average CTP with respect to key system parameters. Moreover, we optimize the caching distribution of the files to maximize the average RTP of the system, while satisfying the constraints on the caching size and the average CTP. Through numerical results, we show that our proposed secure random caching scheme can effectively boost the secrecy performance of the system compared to the existing solutions.

In this paper, we investigate the problem of asynchronous coded caching in fog radio access networks (F-RANs). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents for asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The closed-form expression of the fronthaul load is established for the special case where the number of requests during each time slot is fixed, and the upper and lower bounds of the fronthaul load are given for the general case where the number of requests during each time slot is random. The simulation results show that our proposed scheme can create considerable coded-multicasting opportunities in asynchronous request scenarios.

In this article, the edge caching policy in fog radio access networks (F-RANs) is optimized via deep learning. Considering that it is hard for fog access points (F-APs) to collect sufficient data of massive content features, our proposed edge caching policy only utilizes the number of requests and user location. In an offline phase, we propose to learn the corresponding popularity prediction model for every content popularity trend class and user location prediction models to make the popularity prediction accurate, adaptive and targeted. Moreover, we develop a loss function to avoid overfitting and increase sensitivity to high popularity for popularity prediction models. In an online phase, we propose a reactive caching scheme to react to user requests. In order to guarantee that classification can improve the popularity prediction accuracy in both phases, deep learning and k-Nearest Neighbor (kNN) are combined to classify popularity trends. Besides, a joint proactive-reactive caching policy is proposed to maximize the cache hit rate. The proposed policy is able to promptly track the various popularity trends with spatial-temporal popularity, trend and user dynamics with a low computational complexity. Extensive performance evaluation results show that the cache hit rate of our proposed policy approaches that of the optimal policy.

In this paper, we maximize the system throughput of a point-to-point ultra-reliable low-latency communications (URLLC) system by jointly optimizing its block length and pilot length under the constraints of latency and block error probability. A finite block length (FBL) is adopted to enable low transmission latency. We prove that the throughput is approximately concave with respect to pilot length, given a block length, and that there exists a unique optimal block length in terms of throughput, with a given pilot length. Closed-form expressions are derived for the near-optimal pilot length with a given block length, as well as the asymptotic block error probability with respect to both block length and pilot length. A low-complexity iterative algorithm is proposed for joint optimization of block length and pilot length, which converges within only 1-3 iterations. Simulation results show that the proposed joint optimization scheme achieves a near-optimal throughput performance of an FBL URLLC system, with a much lower complexity than exhaustive search. It also significantly outperforms the previous approaches that considered either block length optimization or pilot length optimization only.

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to make the popularity prediction accurate and adaptive, we propose to learn the corresponding popularity prediction model for every content class from the preprocessed popularity series by training a simplified bidirectional long short-term memory (Bi-LSTM) network, and further use it to help build a content classifier in terms of content popularity trend in the training phase. Then, content popularity can be predicted by the right prediction model with respect to the corresponding content class in the predicting phase. Considering that it is hard to collect enough data about numerous content features through F-APs, we propose to only use the number of requests. Our proposed content popularity prediction policy offers a high prediction accuracy with low computational complexity by transferring the high complexity tasks from the predicting phase to the training phase. Simulation results show that the cache hit rate of our proposed policy approaches the optimal performance.

In this paper, we investigate a spectrum- and energy-efficient emergency wireless communication system with an unmanned aerial vehicle (UAV) mounted base station (BS). To the best of our knowledge, this is the first work to investigate joint optimization of three-dimensional (3D) beamforming (BF), power allocation (PA), user scheduling and UAV trajectory, to maximize the average spectrum efficiency (SE) while maintaining high energy efficiency (EE). Furthermore, a closed- form BF design in both angle- and power-domains is proposed for the first time for UAV-BS assisted wireless systems. Thanks to the closed-form solutions, a low-complexity iterative algorithm is proposed for joint optimization, which converges within only one or two iterations. Simulation results show that 3D BF plays a dominant role in the overall performance, and the proposed closed- form 3D BF design achieves near-optimal performance in terms of both EE and SE, while requiring much lower complexity than exhaustive search. The maximum UAV speed with respect to the optimal EE is also obtained.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated to minimize the average download delay. Considering the non-linear and coupled multi-variable nature of the original optimizing problem, we transform it into an equivalent integer linear programming problem with decoupled variables. Then, we decomposed the transformed problem into two subproblems which can be solved separately by each fog access point (F-AP). Considering the non-deterministic polynomial hard (NP-hard) nature of the two decomposed subproblems, we propose an improved pigeon inspired optimization (PIO) based cooperative edge caching scheme, which utilizes Cauchy perturbation and self-adaptive factor to avoid pre-mature convergence and achieve a better search performance, respectively. Our proposed scheme not only allows F-APs to make cache decisions with low computational complexity, but also has very low message passing overhead. Simulation results show that our proposed scheme can greatly decrease the average download delay.

In this paper, we jointly explore random caching and cooperative transmission based on cooperative radius in HetNets. Using tools from stochastic geometry, we obtain the tractable expression of the average successful transmission probability (STP) under our proposed scheme. By maximizing the average STP, we formulate the optimization problem to find the optimal policy. Given the existence of popularity prediction errors in practice, we then examined two types of errors: errors in file popularity prediction and errors in file library size estimation. By comparing with several existing caching policies in the literature (e.g., the most popular caching and the uniform caching), we demonstrated the robustness of our proposed scheme in the presence of errors.

In this paper, we analyze the performance of a NOMA-based cooperative relaying network with the Nth best relay selection scheme over Rayleigh fading channels. In addition, the simultaneous wireless information and power transfer (SWIPT) technique is applied to prolong the work time of the relay. Some closed-form expressions as well as the asymptotic analysis for the outage probability are derived. These analysis reveals that the diversity order achieved by the Nth best relay selection scheme is M -N +1, where M is the number of all the relays and N is the order number. Monte Carlo simulation results verify our theoretical analysis and demonstrate the impact of the number of the relays, the order number, and the power-splitting ratio on the outage performance of our considered system.

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods.

For a mmWave multiple input multiple output (MIMO) system, the hybrid precoding scheme is of great interest due to its lower power consumption and hardware cost, where a smaller number of RF chains are used to feed a larger scale of antenna array. In this paper we consider a flexible architecture where RF chains and antennae can be dynamically connected, and propose a greedy algorithm to achieve dynamic subarray configuration. The greedy algorithm can select the best subarrays from all the possible candidates in each search process, while the Lanczos algorithm is applied to further reduce the computation complexity. Simulation results demonstrate the superior performance of the proposed algorithm to existing works yet with a lower complexity.

The hybrid beamforming strategy that applies a combination of analog and digital precoders has attracted much attention recently for its ability to reduce both hardware complexity and energy consumption. However due to the large number of antennae, the determination of hybrid precoders usually involves a large amount of computation, which may take much time and potentially risk the low-latency demand. In this paper we propose a novel divide-and-conquer precoding scheme where the precoding problem for the sub-connected architecture is divided into a series of independent subproblems. These subproblems can be solved simultaneously to save time. To deal with the subproblems, we propose a semidefinite relaxation based approach. By dividing the precoding problem, computational complexity can be reduced, and simulation results show its comparable performance with existing works, and robustness against potential saddle points with poor performance.

In this paper, we investigate the optimal caching policy in a K-tier millimeter wave (mmWave) cache- enabled heterogeneous network. In order to mitigate interferences, we incorporate base station (BS) idling into our analysis. Under the random caching framework, we derive the association probability for each tier as well as the successful transmission probability (STP) by utilizing stochastic geometry and taking the blockage effect into account. In addition, we adopt the gradient projection method to obtain the locally optimal caching probabilities and propose a two-stage scheme to obtain the globally optimal caching probabilities under the special case where the LOS ranges for K tiers are sufficiently large. Numerical results demonstrate the superiority of the proposed method over the conventional caching strategies such as Most Popular Content (MPC) and Uniform Caching (UC) schemes.

This paper proposes a new hybrid architecture by using multiple unmanned aerial vehicles enabled aerial base stations (ABSs) to offload data traffic for a single overloaded ground base station (GBS). We consider two practical spectrum sharing strategies, i.e., orthogonal spectrum sharing and nonorthogonal spectrum reusing between GBS and ABSs. For the non-orthogonal spectrum reusing case, we aim to maximize the minimum throughput for cell-edge ground users (GUs) by jointly optimizing the coverage radius of ABSs, and the number of ABSs. For the orthogonal spectrum sharing case, we have also optimized an additional variable, i.e., bandwidth allocation ratio. Numerical results indicate that the orthogonal spectrum sharing strategy outperforms the non-orthogonal spectrum reusing strategy and the conventional GBS-only case, thus provides an attractive solution to offload data traffic for a temporary cellular hotspot.

In this study, cooperative caching is investigated in fog radio access networks. To maximise the offloaded traffic, a cooperative caching optimisation problem is formulated. By analysing the relationship between clustering and cooperation and utilising the solutions of the knapsack problems, the above challenging optimisation problem is transformed into a clustering subproblem and a content placement subproblem. To further reduce complexity, the authors propose an effective graph‐based approach to solve the two subproblems. In the graph‐based clustering approach, a node graph and a weighted graph are constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the objective cluster sets can be readily obtained by using an effective greedy algorithm to search for the max‐weight independent subset. In the graph‐based content placement approach, a redundancy graph is constructed by removing the edges in the complete subgraphs of the node graph corresponding to the obtained cluster sets. Furthermore, they enhance the caching decisions to ensure each duplicate file is cached only once. Compared with traditional approximate solutions, their proposed graph‐based approach has lower complexity. Simulation results show remarkable improvements in terms of offloaded traffic by using the proposed approach.

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.

In this paper, a joint transmitter and receiver design for pattern division multiple access (PDMA) is proposed. At the transmitter, pattern mapping utilizes power allocation to improve the overall sum rate, and beam allocation to enhance the access connectivity. At the receiver, hybrid detection utilizes a spatial filter to suppress the inter-beam interference caused by beam-domain multiplexing, and successive interference cancellation to remove the intra-beam interference caused by power-domain multiplexing. Furthermore, we propose a PDMA joint design approach to optimize pattern mapping based on both the power domain and beam domain. The optimization of power allocation is achieved by maximizing the overall sum rate, and the corresponding optimization problem is shown to be convex theoretically. The optimization of beam allocation is achieved by minimizing the maximum of the inner product of any two beam allocation vectors, and an effective dimension reduction method is proposed through the analysis of pattern structure and proper mathematical manipulations. Simulation results show that the proposed PDMA approach outperforms the orthogonal multiple access and power-domain non-orthogonal multiple access approaches even without any optimization of pattern mapping, and the optimization of beam allocation yields a significant performance improvement than the optimization of power allocation.

In this paper, power control in the uplink for two-tier small-cell networks is investigated. We formulate the power control problem as a Stackelberg game, where the macrocell user equipment (MUE) acts as the leader and the small-cell user equipment (SUE) acts as the follower. To reduce the cross-tier and cotier interferences and the power consumption of both the MUE and SUE, we propose optimizing not only the transmit rate but also the transmit power. The corresponding optimization problems are solved through a two-layer iteration. In the inner iteration, the SUE items (SUEs) compete with each other, and their optimal transmit powers are obtained through iterative computations. In the outer iteration, the optimal transmit power of the MUE is obtained in a closed form based on the transmit powers of the SUEs through proper mathematical manipulations. We prove the convergence of the proposed power control scheme, and we also theoretically show the existence and uniqueness of the Stackelberg equilibrium (SE) in the formulated Stackelberg game. The simulation results show that the proposed power control scheme provides considerable improvements, particularly for the MUE.

With the development of fifth generation (5G) communications systems, heterogeneous networks (HetNets) have become a research hotspot. However, most of the existing work assumes that the user is static in HetNets. This hypothesis can greatly simplify system models, nevertheless it is not always accurate enough, because the user mobility has a significant effect on the performances of HetNets. Therefore, this paper studies the influence of the infinite user mobility in HetNets by using tools from stochastic geometry. The local delay and energy efficiency of HetNets based on Poisson point process (PPP) and Poisson cluster process (PCP) respectively are derived, the latter being more accurate to model the hot spot areas than the former. It was found that user mobility can reduce obviously the local delay and improve the energy efficiency under the high SIR regime. Moreover, the local delay is larger and the energy efficiency is lower under PCP than under PPP for infinite user mobility. In addition, the user mobility can reduce the function of the discontinuous transmission (DTX) scheme, which is used to improve the energy efficiency. Finally, the simulation results verify the accuracy of the numerical analyses.

We propose a blind selective mapping (BSLM) based peak-to-average power ratio (PAPR) reduction method and an independent component analysis (ICA) based channel equalization structure for millimeter wave (mmWave) filter bank multi-carrier (FBMC) with offset quadrature amplitude modulation (OQAM) systems. On the one hand, a new phase adjustment factor is introduced in the BSLM based PAPR reduction scheme, which is spectrum-efficient as the phase ambiguity incurred can be resolved blindly at the receiver, requiring no side information. On the other hand, we propose an ICA based blind equalization scheme with a new phase shift correction approach, which can resolve the phase ambiguity incurred by ICA equalization. Simulation results show that the proposed structure can provide bit error rate (BER) performance close to zero- forcing (ZF) equalization with perfect channel state information (CSI), and the BSLM based PAPR reduction outperforms the existing methods in the literature, while requiring no side information at the receiver.

In this paper, the edge caching problem in ultra-dense fog radio access networks (F-RAN) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a dynamic distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the caching optimization problem as a stochastic differential game (SDG) which captures the temporal dynamics of F-AP states and incorporates user requests status. The SDG is further approximated as a mean field game (MFG) by exploiting the ultra-dense property of F-RAN. In the MFG, each F-AP can optimize its caching policy independently through iteratively solving the corresponding partial differential equations without any information exchange with other F-APs. The simulation results show that the proposed edge caching scheme outperforms the baseline schemes under both static and time-variant user requests.

In this paper, we investigate asynchronous coded caching in fog radio access networks (F-RAN). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents in asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The simulation results show that our proposed scheme creates considerable coded-multicasting opportunities in asynchronous request scenarios.

Heterogeneous cellular networks (HetNets) consist of different tiers of base stations (BSs) to meet the ever-increasing mobile traffic demand. Random deployment of various BSs has mostly been assumed to satisfy a Poisson point process (PPP). However, low power small cells are usually clustered around the popular areas, and PPP does not reflect such a feature. To this end, in this paper, we consider base station (BS) cooperation and analyze user rate and energy efficiency of the HetNets based on a specifical Poisson cluster process (PCP). A calculable formula for spectral efficiency is derived. Simulation results are consistent with the numerical analysis, which confirms the accuracy of theoretical formulas.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled half-duplex mobile relaying system with imperfect channel estimation over uncorrelated Rician fading channels. By assuming that the UAV follows a circular trajectory and applying decode- and-forward relaying strategy, we study the joint design of beamforming and power allocation to improve the end-to-end performance, subject to individual and sum transmit power constraints over source node and UAV relay node. First, beamforming results are obtained based on maximum ratio combing in UAV receiving stage and transmit zero-forcing in UAV forwarding stage. And then, statistical power allocation is performed based on obtained beamforming results. In addition, exact closed-form outage probability expressions are derived. Numerical results demonstrate that joint design of beamforming and statistical power allocation can effectively enhance the end-to-end performance.

In this paper, to make better use of file diversity provided by random caching and improve the successful transmission probability (STP) of a file, we consider retransmissions with random discontinuous transmission (DTX) in a large-scale cache-enabled heterogeneous wireless network (HetNet) employing random caching. We analyze the STP in two mobility scenarios, i.e., the high mobility scenario and the static scenario. In each scenario, by using tools from stochastic geometry and series expansion of some special functions, we obtain the closed-form expressions for the STP in the general and low signal-to-interference ratio (SIR) threshold regimes, respectively. It shows that a larger caching probability corresponds to a higher STP in both scenarios; random DTX can improve the STP in the static scenario and its benefit gradually diminishes when mobility increases. In addition, the asymptotic analysis shows that the diversity gain is jointly affected by random caching and random DTX in both scenarios.

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink large-scale HetNet. We propose two SBS cooperative transmission schemes under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry and adopting appropriate integral transformations, we first derive a tractable expression for the successful transmission probability under each scheme. Then, under each scheme, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, under each scheme, we obtain a locally optimal solution in the general case and a globally optimal solution in some special cases. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching under each scheme achieves a promising successful transmission probability. The analysis and optimization results provide valuable design insights for practical HetNets.

To make better use of file diversity provided by random caching and improve the successful transmission probability (STP) of a file, we consider retransmissions with random discontinuous transmission (DTX) in a large-scale cache-enabled heterogeneous wireless network employing random caching. We analyze and optimize the STP in two mobility scenarios, i.e., the high mobility scenario and the static scenario. First, in each scenario, by using tools from stochastic geometry, we obtain a closed-form expression for the STP in the general signal-to-interference ratio (SIR) threshold regime. The analysis shows that a larger caching probability corresponds to a higher STP in both scenarios; random DTX can improve the STP in the static scenario and its benefit gradually diminishes when mobility increases. In each scenario, we also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. The asymptotic analysis shows that the diversity gain is jointly affected by random caching and random DTX in both scenarios. Then, in each scenario, we consider the maximization of the STP with respect to the caching probability and the BS activity probability, which is a challenging non-convex optimization problem with a complex objective function. In particular, in the high mobility scenario, we obtain a globally optimal solution. In the static scenario, we develop a low-complexity iterative algorithm to obtain a stationary point. Finally, numerical results show that the proposed solutions achieve significant gains over existing baseline schemes and can well adapt to the changes of the system parameters to wisely utilize storage resources and transmission opportunities.

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink large-scale HetNet. We propose an SBS cooperative transmission scheme under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry, we first derive a tractable expression for the successful transmission probability. Then, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, we obtain a local optimal solution in the general case and the global optimal solution in a special case. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching achieves better successful transmission probability performance.

The task of securing the enterprise network currently involves the use of several specialized devices (i.e. 'middleboxes') to provide specific functions in order to satisfy a set of high-level defined objectives. With only a loose common goal of securing the network, these employed devices could not be more distinct in functionality - from network access-control, vulnerability assessment, to intrusion detection and prevention systems, to firewalls. Network operators are therefore faced with the immense challenge of having to manage hundreds to thousands of configuration lines across these devices, in addition to providing the necessary coordination between these disparate functions, in order to effectively secure the network. Towards the aim of unifying policy enforcement across network security functions, we present an architecture that is based on Software-Defined Networking (SDN). Our solution extends the IEEE 802.1X framework and abstracts network endpoint connectivity context, thereby allowing for cross-functional policy composition and enforcement. We present a proof-of-concept prototype and performed some experimental evaluation, as well as outlining our future research directions.

Hybrid analog/digital precoding has been widely utilized in millimeter wave multiple-input multiple-output (MIMO) systems to relieve the severe power consumption of gigasample/s mixed-signal devices. With conventional exhaustive beam searching, however, the beams formed by large antenna arrays can lead to a large time overhead at beam training stage. In this paper, we propose a novel multiuser downlink beam training algorithm to estimate the angle of arrival (AoA) and the angle of departure (AoD) between the base station(BS) and the mobile stations (MSs). The proposed algorithm realizes much reduction of beam training overhead and releases more time resources for users' data transmission.

In this paper, pattern division multiple access with large-scale antenna array (LSA-PDMA) is proposed as a novel non-orthogonal multiple access (NOMA) scheme. In the proposed scheme, pattern is designed in both beam domain and power domain in a joint manner. At the transmitter, pattern mapping utilizes power allocation to improve the system sum rate and beam allocation to enhance the access connectivity and realize the integration of LSA into multiple access spontaneously. At the receiver, hybrid detection of spatial filter (SF) and successive interference cancellation (SIC) is employed to separate the superposed multiple-domain signals. Furthermore, we formulate the sum rate maximization problem to obtain the optimal pattern mapping policy, and the optimization problem is proved to be convex through proper mathematical manipulations. Simulation results show that the proposed LSA-PDMA scheme achieves significant performance gain on system sum rate compared to both the orthogonal multiple access scheme and the power-domain NOMA scheme.

In this paper, energy efficient power allocation for downlink massive MIMO systems is investigated.A constrained non-convex optimization problem is formulated to maximize the energy efficiency (EE), which takes into account the quality of service (QoS) requirements.By exploiting the properties of fractional programming and the lower bound of the user data rate, the non-convex optimization problem is transformed into a convex optimization problem.The Lagrangian dual function method is utilized to convert the constrained convex problem into an unconstrained convex one.Due to the multi-variable coupling problem caused by the intra-user interference, it is intractable to derive an explicit solution to the above optimization problem.Exploiting the standard interference function, we propose an implicit iterative algorithm to solve the unconstrained convex optimization problem and obtain the optimal power allocation scheme.Simulation results show that the proposed iterative algorithm converges in just a few iterations, and demonstrate the impact of the number of users and the number of antennas on the EE.

In this paper, energy efficient power allocation for the uplink of a multi-cell massive MIMO system is investigated. With the simplified power consumption model, the problem of power allocation is formulated as a constrained Markov decision process (CMDP) framework with infinite-horizon expected discounted total reward, which takes into account different quality of service (QoS) requirements for each user terminal (UT). We propose an offline solution containing the value iteration and Q-learning algorithms, which can obtain the global optimum power allocation policy. Simulation results show that our proposed policy performs very close to the ergodic optimal policy.

In this paper, energy efficient power control for the uplink two-tier networks where a macrocell tier with a massive multiple-input multiple-output (MIMO) base station is overlaid with a small cell tier is investigated. We propose a distributed energy efficient power control algorithm which allows each user in the two-tier network taking individual decisions to optimize its own energy efficiency (EE) for the multi-user and multi-cell scenario. The distributed power control algorithm is implemented by decoupling the EE optimization problem into two steps. In the first step, we propose to assign the users on the same resource into the same group and each group can optimize its own EE, respectively. In the second step, multiple power control games based on evolutionary game theory (EGT) are formulated for each group, which allows each user optimizing its own EE. In the EGT-based power control games, each player selects a strategy giving a higher payoff than the average payoff, which can improve the fairness among the users. The proposed algorithm has a linear complexity with respect to the number of subcarriers and the number of cells in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of fairness by using the proposed algorithm.

In this study, the authors first analyse system performance of beamforming amplify‐and‐forward two‐way relay networks with physical‐layer network coding under the impact of co‐channel interference from multiple surrounding terminals and then propose the associated power allocation strategies. The performance of the two‐way communications in terms of outage probability, symbol error rate (SER), and total ergodic channel capacity of the system is quantified. Asymptotic performance analysis for sufficiently high signal‐to‐noise ratio is also provided to obtain further valuable insights into system designs. Based on the analysis, power allocation strategies to minimise the asymptotic outage probability and SER as well as to maximise the ergodic channel capacity under the total power constraint are developed. The numerical results show that the proposed power allocation approaches outperform equal power allocation given the same total power budget and other system parameters.

Heterogeneous cellular networks (HetNets) are to be deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, the dense and random deployment of small cells and their uncoordinated operation raise important concerns about various costs issues, among which notably is energy efficiency. Base station (BS) cooperation is set to play a key role in managing interference in HetNets. In this paper, we consider BS cooperation in the downlink HetNets where BSs from different tiers within the respective cooperative clusters jointly transmit the same data to a typical user, and in particular focus on the optimization of the energy efficiency performance. First, based on a proposed clustering model, we derive the spectral efficiency using tools from stochastic geometry. Furthermore, we formulate a power minimization problem with a minimum spectral efficiency constraint and derive the optimal received signal strength (RSS) thresholds under certain approximation. Building upon these results, we could address the problem of how to design appropriate RSS thresholds, taking into account the tradeoff between spectral efficiency and energy efficiency. Simulations show that the proposed clustering model is more energy-saving than the geometric clustering model, and deploying a multitier HetNet is significantly more energy-saving compared to a macro-only network.

Heterogeneous cellular networks (HetNets) are to be deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, the dense and random deployment of small cells and their uncoordinated operation raise important concerns about energy efficiency. On the other hand, discontinuous transmission (DTX) mode at the base station (BS) serves as an effective technology to improve the energy efficiency of overall system. In this paper, we investigate the energy efficiency under the finite local delay constraint in the downlink HetNets with random DTX scheme. Using a stochastic geometry based model, we derive the local delay and energy efficiency in the general case and obtain closed-form expressions in some special cases. These results give some useful insights on the system performance, taking the tradeoff between local delay and energy efficiency into account. Furthermore, we provide the low-rate and high-rate asymptotic behavior of the maximum energy efficiency. It is analytically shown that it is less energy-efficient to apply random DTX scheme in the low-rate regime. However, in the high-rate regime, random DTX scheme is essential to achieve the finite local delay and higher energy efficiency.

In this paper, we investigate the system performance in terms of outage probability and symbol error rate of cognitive relay networks with underlay spectrum access over Nakagami-m frequency selective fading channels. Underlay spectrum access is deployed at the secondary transmitters, i.e., the secondary source and relay, as a means of providing high spectrum utilization efficiency. It is assumed that the whole system operates in frequency selective fading channels which commonly occur in broadband communication networks. In addition, direct communication from the secondary source to destination is present together with relay communication such that the selection combining is applied at the destination. That is, either the direct or relay channel is selected for communication depending on which one provides the best signal-to-noise ratio (SNR). Analytical expressions for crucial performance measures such as outage probability and symbol error rate are formulated. Based on these analytical outcomes, respective system performance is investigated through numerical results for various system parameters and scenarios.

In this paper, a resource allocation algorithm for maximizing energy efficiency (EE) is studied in multi-cell orthogonal frequency division multiple access (OFDMA) wireless networks. The resource allocation is designed based on imperfect channel state information (CSI). We formulate the resource allocation problem as a mixed non-convex probabilistic optimization problem. The user scheduling, data rate adaptation and power allocation are jointly designed to maximize the system EE, under the maximum transmitted power constraint and the outage probability constraint. An iterative algorithm is proposed in which the EE keeps improving until algorithm convergence. In each iteration, the energy-efficient power allocation optimization problem is solved by a lower bound problem and a parameterized transformation. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm.

Energy efficiency (EE) of wireless communications has attracted growing attention in recent years. In this paper, we focus on the EE optimization in cognitive radio networks (CRN) from a perspective of designing MAC frame structure, namely scheduling the sensing-then-transmission time slots. Considering secondary users adopting channel handoff to avoid collisions with primary users, the EE of CRN is modeled, which is a function of the sensing time and the transmission time. Under the constraint of protecting primary users sufficiently, an EE optimization problem is formulated, and the energy- efficient MAC frame is thus obtained by solving the problem. Simulation results confirm theoretical analysis, i.e., the EE of CRN depends on the sensing capability and interference constraint. Moreover, the proposed optimization method for cognitive MAC frame can enhance the EE of CRN effectively.

This paper investigates the achievable sum rates of multiple-input multiple-output (MIMO) single carrier frequency division multiple access (SC-FDMA) systems with different receivers in spatially uncorrelated frequency selective Rayleigh fading channels. Zero forcing (ZF), linear minimum mean-squared error (MMSE) and the proposed per subcarrier maximum likelihood (PSML) receivers are considered. Closed form expressions for the upper and lower bounds on the achievable sum rates of ZF, MMSE and PSML receivers are derived. Through these expressions, we characterize the behavior of the receivers in various scenarios of different channel lengths and subcarrier numbers. It is found that compared with the optimal receiver, the PSML receiver can obtain the optimal sum rate with significantly reduced computational complexity in flat fading channels. In addition, for ZF or MMSE receivers, either the upper or lower bound can be an accurate approximation of the achievable sum rate under certain conditions and can also provide a theoretical reference to practical systems.

Two‐way relaying systems efficiently accomplish transmissions in both directions within dual‐hop, hence, two time slots can be saved compared with one‐way relaying. However, the conventional two‐way relaying protocol requires the assumption of symmetric traffic demands, that is, each transmitter node has to act as a receiver in latter slot. This assumption restricts applying two‐way relay to general and practical scenarios. In this study, the authors release this unpractical constraint by assuming that the transmitter in slot 1 and receiver in slot 2 can be any nodes, which are not necessarily being the same. For this scenario, a novel transmission protocol exploiting the overhearing link to suppress the interference caused by asymmetric traffic, denoted as overhearing transmission, is proposed. With the overhearing transmission protocol, and in the light of green communications, the precoding matrices at the decode‐and‐forward multi‐antenna relay are optimised to improve energy efficiency in both uplink and downlink (DL) transmission directions, where the objective is to minimise the transmit power at the relay while guaranteeing a target transmission rate. The authors transform the original non‐convex problem to an equivalent form, which can be readily solved by typical semi‐definite relaxation approaches. An efficient algorithm is further proposed to implement the precoding design in practice. Simulation results show that the proposed algorithm is able to minimise the power consumption at the relay, with the minimum rate constraints of both the uplink and DL transmissions being satisfied.

When two terminals exchange information through an intermediate relay, physical‐layer network coding (PLNC) improves the spectrum efficiency by allowing cocurrent packet transmission. Synchronisation is one of the most important issues in distributed wireless communications systems. In time‐domain (TD)‐based PLNC, signals transmitted from user terminals may arrive the relay at different time. The fractional symbol level delay will introduces inter‐symbol interference because of the use of practical pulse‐shaping and matched‐filter. Orthogonal‐frequency division‐multiplexing (OFDM) can be used to deal with time asynchrony. However, OFDM systems are very sensitive to carrier frequency offsets, which will introduce inter‐carrier interference. In this study, a novel low‐complexity symbol‐based decoding iterative interference cancellation schemes are proposed for TD‐based and OFDM‐based PLNC. Signals from two sources are separately decoded, and interference is reconstructed and eliminated. Monte Carlo simulations show that the proposed scheme with over just one iteration can significantly improve the bit error rate performance.

Heterogeneous cellular networks (HetNets) are to be deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, the dense and random deployment of small cells and their uncoordinated operation raise important concerns about energy efficiency. In this paper, we consider the base station (BS) cooperation solution for improving energy efficiency of the HetNets where BSs from each tier within the cooperative cluster jointly transmit the same data to a typical user. Firstly, based on the proposed clustering model, we precisely derive the ergodic rate expression using tools from stochastic geometry. Furthermore, we formulate a power minimization problem with minimum ergodic rate constraint and derive a closed-form approximated result of the optimal cooperative radii. Building upon these results, we could effectively address the problem how to design appropriate cooperative radii, taking into account the trade-off of ergodic rate and energy efficiency. Simulation results also indicate that under the proposed clustering model, deploying a two-tier HetNet is more energy-saving compared to a macro-only network.

Decode-and-forward physical-layer network coding (PLNC) is one of the promising high-performance techniques for wireless relay networks, but little has been reported on the case of asynchronous scenarios using practical pulse shaping waveforms. When signals arrive at the relay with symbol level fractional delay, inter-symbol interference (ISI) will occur. This paper presents an iteration-based ISI cancelation scheme for practical asynchronous two-way relay network (TWRN) with fractional delay. Signals from both terminals are separately decoded for ISI reconstruction and elimination. Simulations show that the proposed scheme with even just one iteration can significantly improve the BER performance.

In this study, the authors focus on the tradeoff between spectrum sensing and energy efficiency of cognitive radio networks (CRN). Considering two interference‐avoidance schemes, that is, channel handoff and stop‐and‐wait, the authors, respectively, model the energy efficiency (EE) of CRN as the mean of the throughput‐to‐power ratio. Research shows that stop‐and‐wait scheme is a special case of channel handoff from an EE perspective. Based on the proposed EE model, the authors build up an EE optimisation problem under the constraint of the sensing quality, and formulate the sensing‐energy efficiency tradeoff (SET) for CRN. The similarity and difference between the SET and the sensing‐throughput tradeoff are also investigated. Simulation analysis confirms that there is indeed an optimal sensing time to make the EE maximum, and it is larger than that for maximum throughput. Another interesting result is that the EE and the throughput of CRN can be enhanced together by optimising MAC frame structure in combination with sensing bandwidth adjustment and power control. Our work provides some insights for green CRN in view of MAC frame optimisation.

To mitigate the inter-carrier interference (ICI) of doubly-selective (DS) fading channels, we consider a hybrid carrier modulation (HCM) system employing the discrete partial fast Fourier transform (DPFFT) demodulation and the banded minimum mean square error (MMSE) equalization in this letter. We first provide the discrete form of partial FFT demodulation, then apply the banded MMSE equalization to suppress the residual interference at the receiver. The proposed algorithm has been demonstrated, via numerical simulations, to be its superior over the single carrier modulation (SCM) system and circularly prefixed orthogonal frequency division multiplexing (OFDM) system over a typical DS channel. Moreover, it represents a good trade-off between computational complexity and performance.

In recent years, research on closed-loop STCs show that with the help of feedback, the achievable rate and/or the diversity order can further be improved in space-time coded systems. In this paper, we study on the optimal closed-loop STCs with low-complexity receivers and any amount of feedback. A unified framework for designing closed-loop STCs is firstly proposed by extending the conventional linear dispersion codes to the cases with feedback. Then a new design method is proposed, where the stochastic gradient descent algorithm is employed to obtain optimal set of LDC candidates particularly for spacetime coded systems with linear minimum mean square error (MMSE) receiver. The proposed method allows for flexible system parameters such as the number of transmit/receive antennas, modulated symbols and the length of codewords thus include most of the existing closed-loop STCs. Simulation results confirm the advantages of the newly-proposed design method of closed-loop STCs.

As a large percentage of the energy required by a cellular network is consumed at base station (BS) sites, how to reduce the energy consumption at BS sites has recently received much attention. In this paper, we propose an energy efficient link adaptation scheme to improve the BS's energy efficiency (EE) for long term evalution (LTE) systems. Based on the traditional spectral- efficiency centered link adaptation scheme, the proposed scheme treats transmit power as a new feedback parameter, aiming to maximize the BS's EE under a certain block error rate constraint. In addition, in order to reduce the transmit power adjustment frequency, a semi-static power control scheme is presented. Simulation results indicate that our proposed schemes can improve the BS's EE significantly.

Decode-and-forward physical layer network coding (PLNC) is one of the promising high-performance techniques for wireless relay networks. This paper studies the limited feedback power control for PLNC. The limited feedback ratios are designed based on the channels' statistical properties. Simulation proves the effectiveness of the proposed scheme. We show that for BPSK modulation, power control with 3 feedback bits can bring a #36;2-3dB#36; gain in terms of SER, and for QPSK modulation power control is only beneficial when it is combined with phase control.

Decode‐and‐forward physical layer network coding (PLNC) is one of the promising high‐performance techniques for wireless relay networks. This study presents a channel and delay estimation algorithm along with a detection scheme for time‐domain two‐way relay network in Rayleigh block fading channels. Preambles are attached for frame‐based synchronisation and channel estimation. Moreover, to achieve low‐complexity estimation, the preambles are designed in Alamouti code structure. The Cramer–Rao bound (CRB) of the channel estimation is given, and simulations show that the mean‐square error of channel estimation of the proposed scheme could reach the CRB. Compared with the traditional approach, the newly proposed combined scheme can achieve almost the same performance, but with significant improvement in computational complexity. At last, bit‐error‐rate performances analysis is given. Simulation validates the authors’ analysis, and shows the upper bound and lower bound are tight.

The discrete Fourier transmission spread OFDM (DFTS-OFDM) based single-carrier frequency division multiple access (SC-FDMA) has been widely adopted due to its lower peak-to-average power ratio (PAPR) of transmit signals compared with OFDM. However, the offset modulation, which has lower PAPR than general modulation, cannot be directly applied into the existing SC-FDMA. When pulse-shaping filters are employed to further reduce the envelope fluctuation of transmit signals of SC-FDMA, the spectral efficiency degrades as well. In order to overcome such limitations of conventional SC-FDMA, this paper for the first time investigated cyclic prefixed OQAM-OFDM (CP-OQAM-OFDM) based SC-FDMA transmission with adjustable user bandwidth and space-time coding. Firstly, we propose CP-OQAM-OFDM transmission with unequally-spaced subbands. We then apply it to SC-FDMA transmission and propose a SC-FDMA scheme with the following features: a) the transmit signal of each user is offset modulated single-carrier with frequency-domain pulse-shaping; b) the bandwidth of each user is adjustable; c) the spectral efficiency does not decrease with increasing roll-off factors. To combat both inter-symbol-interference and multiple access interference in frequency-selective fading channels, a joint linear minimum mean square error frequency domain equalization using {a prior} information with low complexity is developed. Subsequently, we construct space-time codes for the proposed SC-FDMA. Simulation results confirm the powerfulness of the proposed CP-OQAM-OFDM scheme (i.e., effective yet with low complexity).

In this paper, we study the performance of relay selection in a two-way relay network (TWRN) using analog network coding (ANC) with asymmetric traffic requirements at the end terminals under imperfect channel state information (CSI). We derive the system outage probability under Rayleigh flat-fading channels with channel estimation error. Three different power allocation schemes are presented. Simulations validate our analysis and show the performance gain of the proposed schemes.

In this paper, we investigate single-carrier frequency division multiple access (SC-FDMA) transmission with offset quadrature amplitude modulations (OQAM). Firstly, we propose cyclic prefixed OQAM orthogonal frequency division multiplexing (CP-OQAM-OFDM) transmission with unequally-spaced subbands. We then apply it to FDMA transmission and propose a SC-FDMA scheme with the following features: a) the transmit signal of each user is offset modulated single-carrier with frequency-domain pulse-shaping; b) the bandwidth of each user is adjustable; c) the spectral efficiency does not decrease with increasing the roll-off factors. To combat both inter-symbol-interference and multiple access interference in frequency-selective fading channels, a joint linear minimum mean square error frequency domain equalization using a prior information with low complexity is developed. Simulation results confirm the effectiveness of the proposed SC-FDMA transmission.

Decode-and-forward physical layer network coding (PLNC) is one of the promising high-performance techniques for wireless relay networks, but little has been reported on the case of asynchronous senarios. This paper presents a channel and delay estimation algorithm along with a detection scheme for Two-Way-Relay-Network (TWRN) in Rayleigh block- flat-fading channels. Prefix and suffix training sequences are added for frame-based synchronization and channel estimation. The Cramer-Rao Bound (CRB) is given, and simulations show that the mean square error(MSE) of channel estimation of the proposed scheme could reach the CRB. The end-to-end BER performance is also shown in simulation.

This paper presents a new design method for delay-tolerant linear dispersion codes (DT-LDCs) for asynchronous cooperative communication networks. We consider a system that is equipped with linear minimum mean square error (MMSE) receiver. Based on the DT-LDC framework, we propose a new design method to yield DT-LDCs that approach near-optimal capacity as well as minimum average MSE. The proposed design employs stochastic gradient algorithm to guarantee the local optimum. Moreover, it is improved by using simulated annealing type optimization to approach the global optimum. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

In cooperative communication networks, owing to the nodes' arbitrary geographical locations and individual oscillators, the system is fundamentally asynchronous. This will damage some of the key properties of the space-time codes and can lead to substantial performance degradation. In this paper, we study the design of linear dispersion codes (LDCs) for such asynchronous cooperative communication networks. Firstly, the concept of conventional LDCs is extended to the delay-tolerant version and new design criteria are discussed. Then we propose a new design method to yield delay-tolerant LDCs that reach the optimal Jensen's upper bound on ergodic capacity as well as minimum average pairwise error probability. The proposed design employs stochastic gradient algorithm to approach a local optimum. Moreover, it is improved by using simulated annealing type optimization to increase the likelihood of the global optimum. The proposed method allows for flexible number of nodes, receive antennas, modulated symbols and flexible length of codewords. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

In this paper, we study the design of linear dispersion codes (LDCs) for asynchronous cooperative communication networks. Firstly, the concept of conventional LDCs is extended to the delay-tolerant version and new design criteria are discussed. Then we propose a new design method to yield delay-tolerant LDCs that approach near-optimal capacity as well as minimum average pairwise error probability. The proposed design employs stochastic gradient algorithm to guarantee the local optimum. Moreover, it is improved by using simulated annealing type optimization to approach the global optimum. The proposed method allows flexible number of nodes, receive antennas, the length of codewords and the number of modulated symbols. Simulation results confirm the performance of the newly-proposed delay-tolerant LDCs.

This paper proposes a novel interference cancellation algorithm for the two-path succussive relay system using network coding. The two-path succussive relay scheme was proposed recently to achieve full date rate transmission with half-duplex relays. Due to the simultaneous data transmission at the relay and source nodes, the two-path relay suffers from the so-called inter-relay interference (IRI) which may significantly degrade the system performance. In this paper, we propose to use the network coding to remove the IRI such that the interference is first encoded with the network coding at the relay nodes and later removed at the destination. The network coding has low complexity and can well suppress the IRI. Numerical simulations show that the proposed algorithm has better performance than existing approaches.

This paper analyzes the delay performance of Enhanced relay-enabled Distributed Coordination Function (ErDCF) for wireless ad hoc networks under ideal condition and in the presence of transmission errors. Relays are nodes capable of supporting high data rates for other low data rate nodes. In ideal channel ErDCF achieves higher throughput and reduced energy consumption compared to IEEE 802.11 Distributed Coordination Function (DCF). This gain is still maintained in the presence of errors. It is also expected of relays to reduce the delay. However, the impact on the delay behavior of ErDCF under transmission errors is not known. In this work, we have presented the impact of transmission errors on delay. It turns out that under transmission errors of sufficient magnitude to increase dropped packets, packet delay is reduced. This is due to increase in the probability of failure. As a result the packet drop time increases, thus reflecting the throughput degradation.

This paper explores a new technique to calculate and plot the distribution of instantaneous transmit envelope power of OFDMA and SC-FDMA signals from the equation of Probability Density Function (PDF) solved numerically. The Complementary Cumulative Distribution Function (CCDF) of Instantaneous Power to Average Power Ratio (IPAPR) is computed from the structure of the transmit system matrix. This helps intuitively understand the distribution of output signal power if the structure of the transmit system matrix and the constellation used are known. The distribution obtained for OFDMA signal matches complex normal distribution. The results indicate why the CCDF of IPAPR in case of SC-FDMA is better than OFDMA for a given constellation. Finally, with this method it is shown again that cyclic prefixed DS-CDMA system is one case with optimum IPAPR. The insight that this technique provides may be useful in designing area optimised digital and power efficient analogue modules.

Dense deployments of wireless local area networks (WLANs) are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable unless an effective channel assignment scheme is used. In this work, a simple and effective distributed channel assignment (DCA) scheme is proposed. It is shown that in order to maximise throughput, each access point (AP) simply chooses the channel with the minimum number of active neighbour nodes (i.e. nodes associated with neighbouring APs that have packets to send). However, application of such a scheme to practice depends critically on its ability to estimate the number of neighbour nodes in each channel, for which no practical estimator has been proposed before. In view of this, an extended Kalman filter (EKF) estimator and an estimate of the number of nodes by AP are proposed. These not only provide fast and accurate estimates but can also exploit channel switching information of neighbouring APs. Extensive packet level simulation results show that the proposed minimum neighbour and EKF estimator (MINEK) scheme is highly scalable and can provide significant throughput improvement over other channel assignment schemes.

Dense deployments of wireless local area networks (WLANs) are fast becoming a permanent feature of all developed cities around the world. While this increases capacity and coverage, the problem of increased interference, which is exacerbated by the limited number of channels available, can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. In an earlier work, an asynchronous, distributed and dynamic channel assignment scheme has been proposed that (1) is simple to implement, (2) does not require any knowledge of the throughput function, and (3) allows asynchronous channel switching by each access point (AP). In this paper, we present extensive performance evaluation of this scheme when it is deployed in the more practical non-uniform and dynamic topology scenarios. Specifically, we investigate its effectiveness (1) when APs are deployed in a nonuniform fashion resulting in some APs suffering from higher levels of interference than others and (2) when APs are effectively switched `on/off' due to the availability/lack of traffic at different times, which creates a dynamically changing network topology. Simulation results based on actual WLAN topologies show that robust performance gains over other channel assignment schemes can still be achieved even in these realistic scenarios.

The popularity of wireless local area networks (WLANs) has resulted in their dense deployments around the world. While this increases capacity and coverage, the problem of increased interference can severely degrade the performance of WLANs. However, the impact of interference on throughput in dense WLANs with multiple access points (APs) has had very limited prior research. This is believed to be due to 1) the inaccurate assumption that throughput is always a monotonically decreasing function of interference and 2) the prohibitively high complexity of an accurate analytical model. In this work, firstly we provide a useful classification of commonly found interference scenarios. Secondly, we investigate the impact of interference on throughput for each class based on an approach that determines the possibility of parallel transmissions. Extensive packet-level simulations using OPNET have been performed to support the observations made. Interestingly, results have shown that in some topologies, increased interference can lead to higher throughput and vice versa.

This paper analyzes the performance of enhanced relay-enabled distributed coordination function (ErDCF) for wireless ad hoc networks under transmission errors. The idea of ErDCF is to use high data rate nodes to work as relays for the low data rate nodes. ErDCF achieves higher throughput and reduces energy consumption compared to IEEE 802.11 distributed coordination function (DCF) in an ideal channel environment. However, there is a possibility that this expected gain may decrease in the presence of transmission errors. In this work, we modify the saturation throughput model of ErDCF to accurately reflect the impact of transmission errors under different rate combinations. It turns out that the throughput gain of ErDCF can still be maintained under reasonable link quality and distance.

This paper proposes a full interference cancellation (FIC) approach for two-path cooperative communications. Unlike the single relay schemes, the two-path cooperative scheme involves two relay nodes, so that the source can continuously transmit data to the two relays alternatively and the full bandwidth efficiency with respect to the direct transmission can be retained. The two-path relay scheme may however suffer from inter-relay interference which is caused by the simultaneous transmission of the source and one of the relays at any time. In this paper, first the inter-relay interference is expressed as a single recursive term in the received signal, and then the FIC approach is proposed to fully remove the inter-relay interference. The FIC has not only better performance but also less complexity than existing approaches. Numerical examples are also given to verify the proposed approach.

Most research on distributed space-time block coding (D-STBC) has so far focused on the case of 2 relay nodes and assumed that the relay nodes are perfectly synchronised at the symbol level. This paper applies STBC to 4-relay node systems under quasi-synchronisation and derives a new detector based on parallel interference cancellation, which proves to be very effective in suppressing the impact of imperfect synchronisation.

Wireless local area networks (WLANs) have changed the way many of us communicate, work, play and live. Due to its popularity, dense deployments are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable if an effective channel assignment scheme is not used. In this paper, we propose an enhanced asynchronous distributed and dynamic channel assignment scheme that is simple to implement, does not require any knowledge of the throughput function, allows asynchronous channel switching by each access point (AP) and is superior in performance. Simulation results show that our proposed scheme converges much faster than previously reported synchronous schemes, with a reduction in convergence time and channel switches by up to 73.8% and 30.0% respectively.

In this paper we propose an enhanced relay-enabled distributed coordination function (rDCF) for wireless ad hoc networks. The idea of rDCF is to use high data rate nodes to work as relays for the low data rate nodes. The relay helps to increase the throughput and lower overall blocking time of nodes due to faster dual-hop transmission. rDCF achieves higher throughput over IEEE 802.11 distributed coordination function (DCF). The protocol is further enhanced for higher throughput and reduced energy. These enhancements result from the use of a dynamic preamble (i.e. using short preamble for the relay transmission) and also by reducing unnecessary overhearing (by other nodes not involved in transmission). We have modeled the energy consumption of rDCF, showing that rDCF provides an energy efficiency of 21.7% at 50 nodes over 802.11 DCF. Compared with the existing rDCF, the enhanced rDCF (ErDCF) scheme proposed in this paper yields a throughput improvement of 16.54% (at the packet length of 1000 bytes) and an energy saving of 53% at 50 nodes.

Due to its popularity, dense deployments of wireless local area networks (WLANs) are becoming a common feature of many cities around the world. However, with only a limited number of channels available, the problem of increased interference can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. In an earlier work, we proposed an improved asynchronous distributed and dynamic channel assignment scheme that (1) is simple to implement, (2) does not require any knowledge of the throughput function, and (3) allows asynchronous channel switching by each access point (AP). In this paper, we present extensive performance evaluation of the proposed scheme in practical scenarios found in densely populated WLAN deployments. Specifically, we investigate the convergence behaviour of the scheme and how its performance gains vary with different number of available channels and in different deployment densities. We also prove that our scheme is guaranteed to converge in a single iteration when the number of channels is greater than the number of neighbouring APs.

Significant performance gain can potentially be achieved by employing distributed space-time block coding (D-STBC) in ad hoc or mesh networks. So far, however, most research on D-STBC has assumed that cooperative relay nodes are perfectly synchronized. Considering the difficulty in meeting such an assumption in many practical systems, this paper proposes a simple and near-optimum detection scheme for the case of two relay nodes, which proves to be able to handle far greater timing misalignment than the conventional STBC detector.

Most research on distributed space time block coding (STBC) has so far focused on the case of 2 relay nodes and assumed that the relay nodes are perfectly synchronised at the symbol level. By applying STBC to 3- or 4-relay node systems, this paper shows that imperfect synchronisation causes significant performance degradation to the conventional detector. To this end, we propose a new STBC detection solution based on the principle of parallel interference cancellation (PIC). The PIC detector is moderate in computational complexity but is very effective in suppressing the impact of imperfect synchronisation.

One major assumption in all orthogonal space-time block coding (O-STBC) schemes is that the channel remains static over the length of the code word. However, time-selective fading channels do exist, and in such case conventional O-STBC detectors can suffer from a large error floor in the high signal-to-noise ratio (SNR) cases. As a sequel to the authors' previous papers on this subject, this paper aims to eliminate the error floor of the H/sub i/-coded O-STBC system (i = 3 and 4) by employing the techniques of: 1) zero forcing (ZF) and 2) parallel interference cancellation (PIC). It is. shown that for an H/sub i/-coded system the PIC is a much better choice than the ZF in terms of both performance and computational complexity. Compared with the, conventional H/sub i/ detector, the PIC detector incurs a moderately higher computational complexity, but this can well be justified by the enormous improvement.

One major assumption in all orthogonal space-time block coding (O-STBC) schemes is that the channel remains static over the entire length of the codeword. However, time selective fading channels do exist, and in such case the conventional O-STBC detectors can suffer from a large error floor in the high signal-to-noise ratio (SNR) cases. This paper addresses such an issue by introducing a parallel interference cancellation (PIC) based detector for the G/sub i/ coded systems (i=3 and 4).

One major assumption in all space-time block coding (STBC) schemes is that the channel remains static over the length of the codeword. However, time selective fading channels do exist, and in such case the conventional STBC detectors can suffer from a large error floor in the high signal-to-noise ratio cases. As a sequel to our previous papers on this subject, this paper aims to eliminate the error floor of the H/sub i/ coded STBC systems (i = 3 and 4) by employing the technique of parallel interference cancellation (PIC). Compared with the conventional H/sub i/ detector, the PlC detector incurs a moderately higher computational complexity, but this can be justified by the enormous performance improvement.

A key assumption in all orthogonal space-time block coding (O-STBC) schemes is that the channel remains static over the length of the codeword. However, time selective fading channels do exist, and in such case the conventional O-STBC receiver will not function properly: even a relatively small variation in channel state can result in a large error floor. This paper presents an effective solution to this issue for the case of four transmit antennas. At the receiver, a simple zero forcing decoder is derived. To improve the performance of the decoder under certain channel conditions, the original O-STBC encoder is also modified accordingly. Computer simulations confirmed the effectiveness of the new procedure.

Multi-rate multicarrier DS-CDMA is a potentially attractive multiple access method for future wireless networks that must support multimedia, and thus multi-rate, traffic. Considering that high performance detection such as coherent demodulation needs the explicit knowledge of the channel, this paper proposes a subspace-based blind adaptive algorithm for timing acquisition and channel estimation in asynchronous multirate multicarrier DS-CDMA systems, which is applicable to both multicode and variable spreading factor systems.

Multi-rate multicarrier DS-CDMA is a potentially attractive multiple access method for future broadband wireless multimedia networks that must support integrated voice/data traffic. This paper proposes a subspace based channel estimation scheme for multi-rate multicarrier DS-CDMA, which is applicable to both multicode and variable spreading factor systems. The performance of the proposed scheme for these two multi-rate systems is compared via numerical simulations.

This paper proposes a subspace based blind adaptive channel estimation algorithm for dual-rate DS-CDMA systems, which can operate at the low-rate (LR) or high-rate (HR) mode. Simulation results show that the proposed blind adaptive algorithm at the LR mode has a better performance than that at the HR mode, with the cost of an increased computational complexity.

This paper proposes the subspace-based space-time (ST) dual-rate blind linear detectors for synchronous DS/CDMA systems, which can be viewed as the ST extension of our previously presented purely temporal dual-rate blind linear detectors. The theoretical analyses on their performances are also carried out. Finally, the two-stage ST blind detectors are presented, which combine the adaptive purely temporal dual-rate blind MMSE filters with the non-adaptive beamformer. Their adaptive stages with parallel structure converge much faster than the corresponding adaptive ST dual-rate blind MMSE detectors, while having a comparable computational complexity to the latter.

Little has so far been reported on the performance of the near-far resistant CDMA detectors in the presence of the synchronization errors. Starting with the general mathematical model of matched filters, this paper examines the effects of three classes of synchronization errors (i.e. time-delay errors, carrier phase errors, and carrier frequency errors) on the performance (bit error rate and near-far resistance) of an emerging type of near-far resistant coherent DS/SSMA detectors, i.e. the linear decorrelating detector (LDD). For comparison, the corresponding results for the conventional detector are also presented. It is shown that the LDD can still maintain a considerable performance advantage over the conventional detector even when some synchronization errors exist. Finally, several computer simulations are carried out to verify the theoretical conclusions.

This paper presents a novel scheme for near-far resistant CDMA detection: isolation bit insertion (IBI). At the transmitter, isolation bits are inserted into the information bit sequence before modulation, and a practical linear decorrelating detector (LDD) is obtained at the receiver. All the advantages that an LDD theoretically offers are retained and realised in practice. >

This paper proposes a new signaling scheme: orthogonal on-off BPSK (O/sup 3/BPSK), for near-far resistant detection in the asynchronous DS/CDMA systems (up-link). The temporally adjacent bits from different users in the received signals are decoupled by using the on-off signaling, and the original data rate is maintained with no increase in transmission rate by adopting an orthogonal structure. The detector at the receiver is a one-shot linear decorrelating detector, which depends upon neither hard-decision nor specific channel coding. Some computer simulations are shown to confirm the theoretical analysis.

The authors present three third- and three fourth-order cumulant based algorithms for blind deconvolution and identification of the nonminimum phase (NMP) systems. In the algorithms, based on a noncausal AR (autoregressive) model and a theorem relating to the inverse filter coefficients, the problem of blind deconvolution and identification of a NMP system is reduced to that of solving the corresponding set of linear equations. Thus, the uniqueness of the solution can normally be guaranteed. Furthermore, only the diagonal slices of cumulants are employed in the algorithms, which results in the algorithms being simpler and more accurate. A simulation example is presented for the case of unskewed continuous input, and the feasibility and efficiency of the algorithm are confirmed.>